1-s2.0-S0957417414002164-main.pdf


  

MIFS-ND: A Mutual Information-based Feature Selection Method

N. Hoquea,!, D. K. Bhattacharyyaa,!, J. K. Kalitab,!

aDepartment of Computer Science & Engineering, Tezpur University
Napaam, Tezpur-784028, Assam, India

bDepartment of Computer Science, University of Colorado at Colorado Springs
CO 80933-7150, USA

Abstract

Feature selection is used to choose a subset of relevant features for e!ective classification of data.

In high dimensional data classification, the performance of a classifier often depends on the feature

subset used for classification. In this paper, we introduce a greedy feature selection method using

mutual information. This method combines both feature-feature mutual information and feature-

class mutual information to find an optimal subset of features to minimize redundancy and to

maximize relevance among features. The e!ectiveness of the selected feature subset is evaluated

using multiple classifiers on multiple datasets. The performance of our method both in terms

of classification accuracy and execution time performance, has been found significantly high for

twelve real-life datasets of varied dimensionality and number of instances when compared with

several competing feature selection techniques.

Keywords: Features, mutual information, relevance, classification

1. Introduction

Feature selection, also known as variable, attribute, or variable subset selection is used in ma-

chine learning or statistics for selection of a subset of features to construct models for describing

data [1, 2, 3, 4, 5]. Two important aspects of feature selection are: (i) minimum redundancy

and (ii) maximum relevance [6]. Besides these, people use feature selection for dimensional-

ity reduction and data minimization for learning, improving predictive accuracy, and increasing

comprehensibility of models. To satisfy these requirements, two dimensionality reduction ap-

proaches are used, i.e., feature extraction and feature selection [7]. A feature selection method

selects a subset of relevant features from the original feature set, whereas a feature extraction

method creates new features based on combinations or transformations of the original feature

set. Feature selection is used to overcome the curse of dimensionality [8] in a pattern recognition

!Corresponding authors
Email addresses: tonazrul@gmail.com (N. Hoque), dkb@tezu.ernet.in (D. K. Bhattacharyya),

jkalita@uccs.edu (J. K. Kalita)
1Nazrul Hoque is a Senior Research Fellow in the Department of Computer Science and Engineering, Tezpur

University, Napaam, Tezpur, Assam, India.
2Dhruba Kr. Bhattacharyya is a professor in the Department of Computer Science and Engineering, Tezpur

University, India.
3Jugal K. Kalita is a professor in the Department of Computer Science, University of Colorado at Colorado

Springs, USA.

Preprint submitted to Expert systems with applications (Elsevier) April 21, 2014



  

system. The main objective of feature selection is to identify m most informative features out of

the d original features, where m<d.

In the literature, feature selection approaches are widely used prior to or during classification

of data in pattern recognition and data mining in fields as diverse as bioinformatics and network

security. Feature selection approaches are classified into four categories, such as filter approach,

wrapper approach, embedded approach, and hybrid approach.

1. Filter approach [9]: This approach selects a subset of features without using a learning

algorithm. It is used in many datasets where the number of features is high. Filter-based

feature selection methods are faster than wrapper-based methods.

2. Wrapper approach [10]: This approach uses a learning algorithm to evaluate the accuracy

produced by the use of the selected features in classification. Wrapper methods can give high

classification accuracy for particular classifiers, but generally they have high computational

complexity.

3. Embedded approach [9]: This approach performs feature selection during the process of

training and is specific to the applied learning algorithms.

4. Hybrid approach [11]: This approach is a combination of both filter and wrapper-based

methods. The filter approach selects a candidate feature set from the original feature set

and the candidate feature set is refined by the wrapper approach. It exploits the advantages

of these two approaches.

Feature selection plays an important role in network anomaly detection. In a network anomaly

detection system, anomalies are identified in a network by monitoring the behavior of normal

data compared to abnormal ones. The detection system identifies an attack based on behavioral

analysis of features of network tra"c data. Network tra"c data objects may contain protocol

specific header fields, such as source address, source port, destination address, destination port,

protocol type, flags and time to live. Not all this information is equally important to detect an

attack. In addition, if the number of features is high, the classifier usually performs poorly. So,

the selection of an optimum and relevant set of features is important for the classifier to provide

high detection accuracy with low computational cost.

1.1. Applications of Feature Selection

Feature selection is an important step in most classification problems to select an optimal

subset of features to increase the classification accuracy and improve time needed. It is widely

used in many applications of data mining and machine learning, network anomaly detection and

natural language processing.

1.1.1. In Data Mining and Machine Learning

Machine learning is appropriate when a task is defined by a series of cases or examples rather

than in terms of an algorithm or rules. Machine learning is useful in many fields including

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robotics, pattern recognition and bioinformatics. In learning a classifier, a predictor or a learning

algorithm is used to extract information from the behavior of the features of a data object. From

the acquired information, the classifier can predict the class label of a new data object. So, a

feature selection algorithm is used to find an optimal features set that can be used by the predictor

to generate maximal information about the class label of the data object.

1.1.2. In Network Anomaly Detection

Anomaly detection in real-time is a challenging problem for network security researchers and

practitioners. A network packet contains a large number of features and hence, an anomaly detec-

tion system takes a significant amount of time to process features to detect anomaly packets [12].

To overcome the problem we need a feature selection method that can identify the most relevant

features from a network packet. The selected features are used by an intrusion detection system

to classify network packets either as normal or anomalous. The accuracy, detection time and

e!ectiveness of the detection system depend on the input feature set along with other hardware

constraints. Therefore, feature selection methods are used to determine a minimal feature set

which is optimal and does not contain redundant features.

1.1.3. In Text Categorization

In the recent past, content-based document management tasks have become important. Text

categorization assigns a boolean value to a document d and the value determines whether the

document belongs to a category c or not. In document classification, appearance of any word in

a document may be considered a feature. Feature sets used reflect certain properties of the words

and their context within the actual texts. A feature set with most relevant features can predict

the category of the document quickly. In automatic text categorization, the native feature space

contains unique terms (words or phases) of the document which can number in tens, hundreds

or thousands. As a result, operation cost for text categorization is not only time consuming but

also intractable in many cases [13]. Therefore, it is highly desirable to reduce the feature space

for e!ective classification of a document.

1.1.4. In Gene Expression Data Mining

Gene analysis is a topic of great interest associated with specific diagnosis in microarray

study. Microarrays allow monitoring of gene expression for thousands of genes in parallel and

produce enormous valuable data. Due to large dimension and over-fitting problem [14] of gene

expression data, it is very di"cult to obtain a satisfactory classification result by machine learning

techniques. Also, discriminant analysis is used in gene expression data analysis to find e!ective

genes responsible for a particular disease. These facts have given rise to the importance of feature

selection techniques in gene expression data mining [15]. Gene expression data is typically high

dimensional and is error prone. As a result, feature selection techniques applied in gene expression

data analysis especially classification techniques. Feature selection has also been applied as a

preprocessing task in clustering gene expression data in search of co-expressed patterns.

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1.2. Contribution

The main contribution of this paper is a mutual information-based feature subset selection

method to use for complex time series data classification such as network anomaly detection,

pattern classification. The method has been established to perform satisfactorily both in terms

of classification accuracy and execution time for a large number of real life benchmark datasets.

The e!ectiveness of the method is established in terms of classification accuracy exhibited for

several real-life intrusion, text categorization, gene expression and UCI datasets, in association

with some well-known classifiers.

The rest of the paper is organized as follows. In Section 2, we explain related work in brief.

Section 3 formulates the problem. The concept of mutual information and the proposed method

are discussed in Section 4. Experimental results are shown in Section 5. Finally, conclusion and

future work are discussed in Section 6.

2. Related Work

Many feature selection algorithms [16], [17], [18], [19], [20], [21] have been proposed for clas-

sification. The common approach for these algorithms is to search for an optimal set of features

that provides good classification result. Most feature selection algorithms use statistical measures

such as correlation and information gain or a population-based heuristic search approach such

as particle swarm optimization, ant colony optimization, simulated annealing and genetic algo-

rithms. An unsupervised feature subset selection method using feature similarity was proposed

by Mitra et al. [22] to remove redundancy among features. They use a new measure called

maximal information compression index to calculate the similarity between two random variables

for feature selection. Bhatt et al. [23] use fuzzy rough set theory for feature selection based on

natural properties of fuzzy t-norms and t-conorms. A mutual information-based feature selection

algorithm called MIFS is introduced by Battiti [24] to select a subset of features. This algorithm

considers both feature-feature and feature-class mutual information for feature selection. It uses

a greedy technique to select a feature subset that maximizes information about the class label.

Kwak and Cho [25] develop an algorithm called MIFS-U to overcome the limitations of MIFS

to obtain better mutual information between input features and output classes than MIFS. Peng

et al. [26] introduce a mutual information based feature selection method called mRMR (Max-

Relevance and Min-Redundancy) that minimizes redundancy among features and maximizes de-

pendency between a feature subset and a class label. The method consists of two stages. In

the first stage, the method incrementally locates a range of successive feature subsets where a

consistent low classification rate is obtained. The subset with the minimum error rate is used as

a candidate feature subset in stage 2 to compact further using a forward selection and backward

elimination based wrapper method. Estevez et al. [27] propose a mutual information-based fea-

ture selection method as a measure of relevance and redundancy among features. Vignolo et al.

[28] introduce a novel feature selection method based on multi-objective evolutionary wrappers

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Table 1: Symbols used in our method
Symbols Meaning Symbols Meaning

D Dataset d Dimension of dataset
F Original feature set F ! Optimal feature set
C Class label k Number of features in F !

fi Feature no i fj Feature no j
Cd Domination count Fd Dominated count
MI Mutual Information FFMI Feature Feature Mutual Information

FCMI Feature Class Mutual Information AFFMI Average Feature Feature Mutual Information

using genetic algorithm.

To support e!ective data classification, several attribute evaluation techniques, such as ReliefF

[29], Chi squared [30], Correlation Feature Selection (CFS) [31] and Principal components analysis

[32], also have been provided in the Weka software platform [33]. These techniques employ dif-

ferent search techniques such as, BestFirst, ExhaustiveSearch, GreedyStepwise, RandomSearch,

and Ranker for feature ranking. To describe the algorithms in this paper, we use symbols and

notations given in Table 1.

2.1. Motivation

Feature selection or attribute selection is an important area of research in knowledge discovery

and data mining. Due to rapid increase in the availability of datasets with numerous data types,

an e!ective feature selection method is absolutely necessary for classification of data in high

dimensional datasets. In many application domains, such as network security or bioinformatics,

feature selection plays a significant role in the classification of data objects with high classification

accuracy. Although a large number of classification and feature selection techniques have been

used in the past, significant reduction of false alarms, especially in the network security domain

remains a major and persistent issue. Decrease in the number of irrelevant features leads to

reduction in computation time. This has motivated us to design a mutual information based

feature selection method to identify an optimal subset of features which gives the best possible

classification accuracy.

3. Problem Formulation

For a given dataset D of dimension d, with a feature set F = {f1, f2, · · · , fd}, the problem is

to select an optimal subset of relevant features F " where (i) F " ! F and (ii) for F ", a classifier

gives the best possible classification accuracy. In other words, we aim to identify a subset of

features where for any pair (fi, fj) " F ", the feature-feature mutual information is minimum and

feature-class mutual information is maximum.

4. Proposed Method

The proposed method uses mutual information theory to select a subset of relevant features.

The method considers both feature-feature and feature-class mutual information to determine an

optimal set of features.

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4.1. Mutual Information

In information theory, mutual information I(X; Y ) is the amount of uncertainty in X due to

the knowledge of Y [34, 35]. Mathematically, mutual information is defined as

I(X; Y ) =
!

x,y

p(x, y)log
p(x, y)

p(x)p(y)
(1)

where p(x, y) is the joint probability distribution function of X and Y , and p(x) and p(y) are the

marginal probability distribution functions for X and Y . We can also say

I(X; Y ) = H(X) # H(X|Y ) (2)

where, H(X) is the marginal entropy, H(X|Y ) is the conditional entropy, and H(X; Y ) is the joint

entropy of X and Y . If H(X) represents the measure of uncertainty about a random variable,

then H(X|Y ) measures what Y does not say about X. This is the amount of uncertainty in

X after knowing Y and this substantiates the intuitive meaning of mutual information as the

amount of information that knowing either variable provides about the other. In our method, a

mutual information measure is used to calculate the information gain among features as well as

between feature and class attributes. Using a greedy manner, each time we pick a feature from

the feature set still left one that provides maximum information about the class attribute with

minimum redundancy.

4.2. Our Feature Selection Method

Initially, we compute feature-class mutual information and select the feature that has the

highest mutual information. The feature is then put into the selected feature subset and removed

from the original feature set. Next, for each of the non-selected features, we compute the feature-

class mutual information and then calculate the average feature-feature mutual information for

each of the selected features. At this point, each non-selected feature contains feature-class mutual

information and average feature-feature mutual information. From these calculated values, it

selects a feature that has the highest feature-class mutual information, but minimum feature-

feature mutual information using an optimization algorithm known as Non-dominated Sorting

Genetic Algorithm-II (NSGA-II) [36] with domination count Cd and dominated count Fd for

feature-class and feature-feature values, respectively. The domination count of a feature represents

the number of features that it dominates for feature-class mutual information and dominated count

represents the number of features that it dominates for feature-feature mutual information. We

select the feature that has the maximum di!erence of domination count and dominated count.

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Table 2: Domination count of a feature
feature feature Average feature Domination Dominated Cd # Fd

class MI -feature MI count Cd count Fd
f1 0.54 0.17 2 1 1
f2 0.76 0.09 3 0 3
f4 0.17 0.78 0 3 -3
f5 0.33 0.27 1 2 -1

4.2.1. Benefits of Competitive Ranking

The proposed feature selection method selects features based on a ranking procedure using the

NSGA-II algorithm. It selects a feature that has the highest feature-class mutual information but

minimum feature-feature mutual information, which requires solving an optimization problem.

The soundness of the proposed method is derived from the fact that it selects a feature which

is either strongly relevant or weakly redundant (explained in Example 1 and Example 2). A

strongly relevant feature has high feature-class mutual information but low feature-feature mutual

information and a weakly relevant feature has high feature-class mutual information but medium

feature-feature mutual information [37]. In addition, the method handles the tie condition (i.e.,

if two features have the same rank) by selecting the feature that has high feature-class mutual

information.

4.2.2. Example 1

Let F = {f1, f2, f3, f4, f5} be a set of five features. First, we compute feature-class mutual

information for every feature and select the feature that has the maximum mutual information

value. Let us assume feature f3 has the highest mutual information value and hence, f3 is removed

from F and is put in the optimal feature set, say F ". Next, for a feature fj " F , we compute

feature-feature mutual information with every feature fi " F " and store the average mutual

information value as average feature-feature mutual information for fj. This way, we compute

feature-feature mutual information for f1, f2, f4 and f5. Again, we compute feature-class mutual

information for f1, f2, f4 and f5. Consider the scenario shown in Table 2. Here, feature f2 has

the maximum di!erence between Cd and Fd, i.e., 3. Hence feature f2 will be selected. In case

of tie for the values of (Cd - Fd), we pick the feature that has maximum feature-class mutual

information. This procedure is continued until we get a subset of k features.

4.2.3. Example 2

For the situation shown in Table 3, the method selects the weakly relevant feature f1 instead

of feature F5. Though the two features have the same Cd - Fd value, F1 has higher feature-class

mutual information but medium feature-feature mutual information. In such a tie situation, the

other methods may select either F1 or F5. According to Peng’s [26] and Battiti’s [24] method a

tie condition for non-selected features X1, X2 and a selected features set Xk with class label Y is

given by the following.

I(X1; Y ) # !
"n#1

k=1 I(X1; Xk) = I(X2; Y ) # !
"n#1

k=1 I(X2; Xk)

I(X1; Y ) +
"n#1

k=1 I(X2; Xk) = I(X2; Y ) +
"n#1

k=1 I(X1; Xk)

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Table 3: Domination count of a feature
feature feature Average feature Domination Dominated Cd # Fd

class MI -feature MI count Cd count Fd
f1 0.96 0.43 3 1 2
f2 0.72 0.45 0 2 -2
f4 0.85 0.78 1 3 -2
f5 0.91 0.10 2 0 2

if I(X1; Y )>I(X2; Y ) $
"n#1

k=1 I(X2; Xk)<
"n#1

k=1 I(X1; Xk)

In this scenario, the proposed method selects the feature X1, since its feature-class mutual infor-

mation is higher than X2.

Definition 1. Feature relevance: Let F be a full set of features, fi a feature and Si = F #{fi}.

Feature fi is strongly relevant i! I(C|fi, Si) %= I(C|Si) otherwise if I(C|fi, Si) = I(C|Si) and

&S"i ' Si such that I(C|fi, Si) %= I(C|Si), then fi is weakly relevant to the class C.

Definition 2. Feature-class relevance: It is defined as the degree of feature-class mutual in-

formation for a given class Ci of a given dataset D, where data elements described by d features.

A feature fi " F ", is an optimal subset of relevant features for Ci, if the relevance of (fi, Ci) is

high.

Definition 3. Relevance score: The relevance score of a feature fi is the degree of relevance

in terms of mutual information between a feature and a class label. Based on this value, a rank

can be assigned to each feature fi, (i = 1, 2, 3, · · · , d. For a given feature fi " F ", the relevance

score is high.

The following properties are trivial based on [26] and [38]

Property 1: For a pair of features (fi, fj) " F ", the feature-feature mutual information is low,

while feature-class mutual information is high for a given class Ci of a given dataset D.

Explanation: For a feature fi to be a member of an optimal subset of relevant features F
" for a

given class Ci, the feature-class mutual information or the relevance score (Definition 3) must be

high. On the contrary, whenever feature-class mutual information is high, feature-feature mutual

information has to be relatively low [38].

Property 2: A feature f"i /" F
" has low relevance w.r.t. a given class Ci of a given dataset D.

Explanation: Let f"i /" F
" where F " corresponds to class Ci and let feature-class mutual informa-

tion between f"i and Ci be high. If the feature-class mutual information score is high, then as per

Definitions 1 and 2, f"i must be a member of set F
", which contradicts.

Property 3: If a feature fi has higher mutual information than feature fj with the class label C,

then fi will have a smaller probability of misclassification [39].

Explanation: Since fi has higher mutual information score thanfj corresponding to class C, it

has more relevance. So, the probability of classification accuracy using fi as one of the feature

will be higher than fj.

Lemma 1: For a feature fi, if the domination count Cd is larger and the dominated count Fd is

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smaller than all other features fj, (i %= j), then the feature fi has the highest feature-class mutual

information and has more relevant.

Proof: For a feature fi, if its domination count Cd is larger and the dominated count Fd is smaller

than all other features fj, (i %= j) then NSGA-II method ensures that feature-class mutual infor-

mation for Fi is the highest whereas the feature-feature mutual information value is the lowest.

Hence, the method selects feature fi as the strongly relevant feature as shown in Example 1.

Lemma 2: For any two features, fi and fj, if the di!erence of Cd and Fd for feature fi is the

same as the di!erence of Cd and Fd for feature fj, then the feature with the highest feature-class

mutual information is relevant.

Proof: For any two features fi and fj, if the di!erence of Cd and Fd for feature fi is as same as

the di!erence of Cd and Fd for feature fj, then NSGA-II ensures that neither fi nor fj satisfies

the Lemma 1 and in this situation either fi or fj has higher feature-class mutual information.

Hence, the method selects a feature that has higher feature-class mutual information as shown in

Example 2.

4.3. MIFS(MI based Feature Selection) method

Input: d, the number of features; dataset D; F = {f1, f2, · · · , fd}, the set of features
Output: F ", an optimal subset of features
Steps:
for i=1 to d, do

Compute MI(fi, C)
end
Select the feature fi with maximum MI(fi, C)
F " = F " ) {fi}
F = F # {fi}
count=1;
while count <= k do

for each feature fj " F, do
FFMI=0;
for each feature fi " F ", do

FFMI=FFMI+compute FFMI(fi,fj)
end
AFFMI=Average FFMI for feature fj.
FCMI=Compute FCMI(fj, C)

end
Select the next feature fj that has maximum AFFMI but minimum FCMI
F " = F " ) {fi}
F = F # {fj}
i = j
count=count+1;

end
Return features set F "

Algorithm 1: MIFS-ND

The proposed feature selection method depends on two major modules, namely Compute FFMI

and Compute FCMI. We describe working of each of these modules next.

Compute FFMI(fi,fj): For any two features fi, fj " F , this module computes mutual informa-

tion between them, i.e.; fi and fj using Equation 1. It computes marginal entropy for variable fi

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and computes mutual information by subtracting conditional entropy of fi for the given variable

fj from marginal entropy.

Compute FCMI(fj, C): For a given feature fj " F and a given class label say C, this module is

used to find mutual information between fj and C using Shannon’s mutual information formula

using equation 1. First, it computes marginal entropy for the variable fj and then subtracts

conditional entropy of fj for the given variable C.

Using these two modules the proposed method picks up a high ranked feature which is strongly

relevant but non-redundant. To select a strongly relevant but non-redundant feature it uses

NSGA-II method and computes the domination count (Cd) and dominated count (Fd) for every

feature. If a feature has the highest di!erence between Cd and Fd then it selects that feature

using Lemma 1 otherwise it uses Lemma 2 to select the relevant feature.

4.4. Complexity Analysis

The overall complexity of the proposed algorithm depends on the dimensionality of the input

dataset. For any dataset of dimension d, the computational complexity of our algorithm to select

a subset of relevance features is O(d2). However, the use of appropriate domain specific heuristics

can help to reduce the complexity significantly.

4.5. Comparison with other Relevant Work

The proposed feature selection method di!ers from other methods in the following manners.

1. Like Battiti [24], our method considers both feature-feature and feature-class mutual in-

formation, but Battiti uses an additional input parameter called ! to regulate the relative

importance of mutual information between a feature to be selected and the features that

have already been selected. Instead of the input !, our method calculates the domina-

tion count and dominated count, which are used in NSGA-II algorithm to select a relevant

feature.

2. Like Kwak and Choi [25], our method uses a greedy filter approach to select a subset of

features. However, like them our method does not consider joint mutual information among

three parameters, such as a feature that is to be selected, a set of features that are already

selected and the class output.

3. Unlike Peng et al. [26], who use both filter and wrapper approaches, our method uses only

the filter approach to select an optimal feature subset using a ranking statistic, which makes

our scheme more computationally cost e!ective. Similar to the Peng’s mRMR method, we

use the maximum relevance and minimum redundancy criterion to select a feature from

the original feature set. In the subsequent section (Section 5) a detailed comparison of

our method with Peng et al.’s method for two high dimensional gene expression datasets

and four UCI datasets has been shown. Unlike Brown [40], our method uses Shannon’s

mutual information on two variables only but Brown’s method uses multivariate mutual

information. Also, our method selects a feature set using a heuristic bottom-up approach

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and iteratively inserts a relevant but non-redundant feature into the selected feature set.

Brown’s method follows a top-down approach and discards features from the original feature

set.

4. Unlike Kraskov et al. [35], our method computes Shannon’s entropy whereas they compute

entropy using k-nearest neighbor distance.

5. Experimental Result

Experiments were carried out on a workstation with 12 GB main memory, 2.26 Intel(R) Xeon

processor and 64-bit Windows 7 operating system. We implement our algorithm using MATLAB

R2008a software.

5.1. Dataset Description

During our experimental analysis, we use several network intrusion, text categorization, a few

selected UCI and gene expression datasets. These datasets contain both numerical and categorical

values with various dimensionalities and numbers of instances. Descriptions of the datasets are

given Table 4.

Table 4: Dataset description

Dataset Number of instances Number of attributes
Intrusion NSL-KDD 99 125973 42
Dataset 10% KDD 99 494021 42

Corrected KDD 311029 42
Wine 178 13

Non-intrusion Monk1 432 6
Dataset Monk2/ Monk3 432 6

IRIS 150 4
Ionosphere 351 34
Sonar 209 61

Text categorization Bloggender-male 3232 101
Bloggender-female 3232 101

Gene Expression Lymphoma 45 4026
Colon Cancer 62 2000

5.2. Results

The proposed algorithm first selects a subset of relevant features from each dataset. To eval-

uate the performance of our algorithm we use four well known classifiers, namely, Decision Trees,

Random Forests, K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The perfor-

mance of our algorithm is compared with five standard feature selection algorithms, namely Chi

squared method, Gain Ratio, Information Gain, ReliefF, and Symmetric Uncertainty, which are

already available in Weka. We use 10-fold cross validation to evaluate the e!ectiveness of selected

features using di!erent classifiers. The comparison of classification accuracy of our algorithm

with all the aforesaid feature selection algorithms is shown here for all the mentioned datasets.

From the experimental results, we observe that the proposed method gives good classification

accuracy on most datasets. Since, the network datasets are very large to be handled in MATLAB,

we split the network datasets into smaller partitions which contain class labels from all the classes.

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Finally, we compute the average classification accuracy of all the partitioned datasets.

The proposed feature selection method selects an optimal subset of features for which we achieve

the best classification accuracy. However, the cardinality of a feature subset identified by the

proposed MIFS-ND method varies for di!erent datasets. This variation in cardinalities of the

optimal feature subsets for some of the datasets, namely, Sonar, Ionosphere, Wine and Iris is

shown in Figures 3(a), 3(b), 4(a) and 4(b), respectively. Our observation is that (i) except for the

Sonar dataset, the proposed MIFS-ND method performs significantly better for other datasets

than the competing methods. The performance of our method su!ers in case of Sonar dataset

due to lack of su"cient training instances, and (ii) with the increase in size of the original feature

sets of di!erent datasets, the cardinality for optimal feature subset identified by MIFS-ND also

increases. For example, in case of the UCI datasets, when the size of feature set varies from 4 to

61 (as shown in Table 4) the cardinality of the optimal feature subset varies from 3 to 6, whereas

in case of the intrusion and text categorization datasets, the cardinality of the feature subsets

varies from 5 to 10 due to the increased size of feature sets. Figure 1 establishes this fact for 12

datasets.

Figure 1: Optimal range of the size of feature subsets for di!erent datasets

5.3. Discussion

Feature selection is an unavoidable part for any classification algorithm. We use mutual

information theory to select a subset of features from an original feature set based on feature-

feature and feature-class information values. We evaluate our feature selection method using

network security, text categorization, a few selected UCI and gene expression datasets. From

experimental analysis, we observe that for most datasets, the classification accuracy is much

better for a subset of features compared to when using the full feature set. Due to significant

reduction of the number of features, better computational e"ciency is also achieved. As shown

in Figure 2, the computational time increases significantly for Random forests but computational

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time is constant for SVMs. The computational time is relatively increased for Decision Trees and

KNN classifiers due to the increase in the number of features.

Figure 2: Execution time performance for di!erent classifiers

5.3.1. Performance Analysis on Intrusion Datasets

In case of network datasets, we found high classification accuracy for all the classifiers with

the 10% KDD dataset as shown in Figures 6(e) to 6(h). We found better classification accuracy

using Decision Trees, Random Forests and the KNN classifier than the SVM on the corrected

KDD dataset for the proposed algorithm as shown in Figures 6(a) to 6(d). Similarly, as shown in

Figures 6(i) to 6(l), our algorithm produces better result on the NSL KDD dataset when k * 6,

where k is the number of features in a subset and when k + 5, the classification accuracy is

relatively lower.

5.3.2. Performance Analysis on UCI Datasets

We perform experiments to analyze the classification accuracy of our proposed MIFS-ND

method on UCI datasets. With the Wine dataset, Decision Trees, Random Forests and the KNN

classifier give better classification accuracy for the proposed method but the accuracy is a bit

lower with the SVM classifier, as plotted in Figures 5(a), 5(b), 5(c) and 5(d), respectively. As

shown in Figures 5(e) to 5(t), the used classifiers show high classification accuracy with Monk1,

Monk2, Monk3 and Iris datasets. We also compare the performance of our method MIFS-ND

with MIFS [24] and MIFS-U [25] for Sonar and Ionosphere datasets as shown in Figure 3(a)

and Figure 3(b), respectively. The comparison shows that the proposed algorithm gives better

classification accuracy with these two UCI datasets.

5.3.3. Performance Analysis on Text Categorization Datasets

The proposed feature selection method is also applied to text datasets to evaluate its per-

formance. From experimental results, we observe that the Bloggender female and male datasets

13



  

show a bit poorer classification accuracy for Decision Trees as shown in Figures 7(a) and 7(d),

respectively. But, the method shows almost equal classification accuracy for Random forests and

KNN as plotted in Figures 7(b), 7(c), 7(e) and 7(f), respectively.

5.3.4. Performance Analysis on Gene Expression Datasets

The proposed MIFS-ND method is also tested on two high-dimensional gene expression datasets,

namely, Lymphoma and Colon Cancer. The classification accuracy on the Lymphoma dataset

is very good for all the four classifiers as shown in Figures 8(a)-8(d). Compared to mRMR,

MIFS-ND gives high classification accuracy for all four classifiers. However, the accuracy is com-

promised from feature number 7 for SVM classifier only. Similarly, in Colon Cancer dataset,

Random forests give high classification accuracy for MIFS-ND as shown in Figure 8(f) compared

to other feature selection methods. But KNN and SVM give high classification accuracy for the

mRMR method as plotted in Figure 8(g) and Figure 8(h), respectively. In case of Decision Tree,

classification accuracy for mRMR and MIFS-ND is almost similar as shown in Figure 8(e).

(a) Ionosphere dataset (b) Sonar dataset

Figure 3: Comparison of MIFS-ND with MIFS, MIFS-U and mRMR for Ionosphere and Sonar datasets

(a) Wine dataset (b) Iris dataset

Figure 4: Comparison of MIFS-ND with Entropy measure, GINI info, ICA-MI, Local ICA-MI and mRMR

5.4. Evaluation of Classification Performance based on t-Test

We use the McNemar’s test [41] to evaluate the performance of our method in terms of clas-

sification accuracy using four di!erent classifiers. According to McNemar’s test, two algorithms

14



  

can have four possible outcomes such as TT, TF, FT and FF. Here, T and F stand for true and

false respectively. The test computes a value called z-score, defined in Equation 3 that measures

how statistically significant the results are. The arrows (,, -) indicate which classifier performs

better with a given dataset.

z =
(|NT F # NF T | # 1).

NT F + NF T
(3)

where, N= number of instances in a dataset.

From the results of t-test, it can be observed that for Wine dataset, the Decision Trees performance

is better than KNN and SVM but Random Forests perform better than Decision Trees for all the

feature selection methods as shown in Table 5. In Monk1 dataset, KNN and Random Forests, both

give better performance than Decision Trees, whereas Decision trees performance is better than

SVM as shown in Table 6. In Monk2, Monk3 and Ionosphere dataset, Decision Trees classifier

show better performance compared to KNN and SVM whereas Random Forests performance is

better than Decision Trees as shown in Table 7, 8 and 11. As given in Table 9, Decision Tree

performance is better than SVM but KNN and Random Forests show better performance than

Decision Trees. In Sonar dataset, the performance of KNN and Random Forests is better than

Decision Trees whereas Decision Trees performance is better than that of SVM as shown in Table

10.

Table 5: t-test for Wine dataset
Dataset Feature

selection KNN RF SVM
method

Wine

ChiSquar

DT

$ 4.3410 1.5539 % $ 8.7818
GainRatio $ 3.1201 2.2159 % $ 8.6927
InfoGain $3.4758 1.3790 % $ 8.8202
ReliefF $ 3.7773 2.4859 % $ 8.6646

SymmetricU $2.9723 1.9829% $ 8.4879
MIFS-ND $2.7097 2.6000 % $ 8.4917

Table 6: t-test for Monk1 dataset
Dataset Feature

selection KNN RF SVM
method

Monk1

ChiSquar

DT

3.1888 % 4.2504 % $ 12.6320
GainRatio 3.2269 % 4.4338 % $ 12.5119
InfoGain $1.8995 1.8943% $10.7272
ReliefF 5.5811 % 5.5811 % $ 12.2406

SymmetricU 3.7420% 4.3032% $ 12.6151
MIFS-ND 2.7419 % 3.8916 % $ 12.4085

Table 7: t-test for Monk2 dataset
Dataset Feature

selection KNN RF SVM
method

Monk2

ChiSquar

DT

$ 2.3840 $ 1.0071 $ 11.0991
GainRatio $ 2.2699 $ 0.5800 $ 10.9840
InfoGain $ 1.7675 $ 1.2980 $ 10.9856
ReliefF 0.7857 % 0.2196% $ 11.2472

SymmetricU $ 2.8095 $1.4083 $ 11.2910
MIFS-ND 0.6572 % 0.5255 % $ 11.0219

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Table 8: t-test for Monk3 dataset
Dataset Feature

selection KNN RF SVM
method

Monk3

ChiSquar

DT

$ 2.6415 $ 6.4908 $ 13.8191
GainRatio $ 2.4969 6.4944 % $ 13.9999
InfoGain $ 1.3687 6.0628 % $ 13.7995
ReliefF 0.6791 % 6.5169 % $ 13.7592

SymmetricU $ 2.3657 6.2318 % $ 13.8910
MIFS-ND $1.2637 $ 5.9339 $ 13.7014

Table 9: t-test for Iris dataset
Dataset Feature

selection KNN RF SVM
method

Iris

ChiSquar

DT

0.1215 % 1.5815 % $ 8.2768
GainRatio 0.1250 % 1.7166 % $ 8.2622
InfoGain 0.1768 % 1.6919 % $ 8.2618
ReliefF $ 0.1250 1.2877 % $ 8.3372

SymmetricU 0.6637 % 1.4462 % $ 8.3071
MIFS-ND 0.2754 % 1.6303 % $ 8.2772

Table 10: t-test for Sonar dataset
Dataset Feature

selection KNN RF SVM
method

Sonar

ChiSquar

DT

0.7063 % 1.3416 % $ 7.2530
GainRatio 1.6916 % 1.6897 % $ 7.5730
InfoGain 1.8612 % 1.4681 % $ 6.6444
ReliefF 1.4308 % 1.8329 % $ 7.0064

SymmetricU 1.8296 % 2.5176 % $ 6.9518
MIFS-ND 1.7365 % 1.8857 % $ 7.2041

Table 11: t-test for Ionosphere dataset
Dataset Feature

selection KNN RF SVM
method

Ionosphere

ChiSquar

DT

$ 0.8715 1.4005 % $ 9.9160
GainRatio $ 0.8175 1.3189 % $ 9.8643
InfoGain $ 0.4200 1.6768 % $ 9.7896
ReliefF $ 0.7503 1.7440 % $ 9.8320

SymmetricU $ 0.6462 1.9885 % $ 9.7559
MIFS-ND 1.6916 % 1.6897 % $ 7.5730

6. Conclusion and Future Work

In this paper, we have described an e!ective feature selection method to select a subset of

high ranked features, which are strongly relevant but non-redundant for a wide variety of real-life

dataset. To select high ranked features, an optimization criterion used in the NSGA-II algorithm

is applied here. The method has been evaluated in terms of classification accuracy as well as

execution time performance using several network intrusion datasets, a few UCI datasets, text

categorization datasets and two gene expression datasets. We compare the classification accuracy

for the selected features using Decision trees, Random Forests, KNN and SVM classifiers. The

overall performance of our method has been found excellent in terms of both classification accuracy

and execution time performance for all these datasets. Development of an incremental feature

selection tool based on MIFS-ND is underway to support wide variety of application domains.

Acknowledgment

This work is supported by Department of Information Technology (DIT). The authors are

thankful to the funding agency.

16



  

(a) Decision Trees accuracy WINE (b) Random Forests accuracy for WINE

(c) KNN accuracy for WINE (d) SVM Accuracy for WINE

(e) Decision Trees accuracy MONK1 (f) Random Forests accuracy for MONK1

(g) KNN accuracy for MONK1 (h) SVM Accuracy for MONK1

(i) Decision Trees accuracy MONK2 (j) Random Forests accuracy for MONK2

17



  

(k) KNN accuracy for MONK2 (l) SVM Accuracy for MONK2

(m) Decision Trees accuracy MONK3 (n) Random Forests accuracy for MONK3

(o) KNN accuracy for MONK3 (p) SVM Accuracy for MONK3

(q) Decision Trees accuracy IRIS (r) Random Forests accuracy for IRIS

(s) KNN accuracy for IRIS (t) SVM Accuracy for IRIS

18



  

(u) Decision Trees accuracy Ionosphere (v) Random Forests accuracy for Iono-
sphere

(w) KNN accuracy for Ionosphere (x) Decision Trees accuracy Sonar

(y) Random Forests accuracy for Sonar (z) KNN accuracy for Sonar

Figure 5: Accuracy of di!erent classifiers found in non-intrusion datasets

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(a) Decision Trees accuracy CKDD (b) Random Forests accuracy for CKDD

(c) KNN accuracy for CKDD (d) SVM Accuracy for CKDD

(e) Decision Trees accuracy 10%KDD (f) Random Forests accuracy for 10%KDD

(g) KNN accuracy for 10%KDD (h) SVM Accuracy for 10%KDD

(i) Decision Trees accuracy NSL KDD (j) Random Forests accuracy for NSL KDD

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(k) KNN accuracy NSL KDD (l) SVM accuracy for NSL KDD

Figure 6: Accuracy of di!erent classifiers found in non-intrusion datasets

(a) Decision Trees accuracy Bloggender Fe-
male

(b) Random Forests accuracy for Bloggender
Feamle

(c) KNN accuracy for Bloggender Female (d) Decision Trees Accuracy for Bloggender
Male

(e) Random Forests accuracy Bloggender Male (f) KNN accuracy for Bloggender Male

Figure 7: Accuracy of di!erent classifiers found in Text Categorization datasets

24



  
(a) Decision Trees accuracy for Lymphoma (b) Random Forests accuracy for Lym-

phoma

(c) KNN accuracy for Lymphoma (d) SVM accuracy for Lymphoma

(e) Decision Trees Accuracy for Colon Can-
cer

(f) Random Forests accuracy Colon Cancer

(g) KNN accuracy for Colon Cancer (h) SVM accuracy for Colon Cancer

Figure 8: Accuracy of di!erent classifiers for Gene Expression datasets

25