id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_oi5ldfwr25f2lm4vqqtv6sk5h4	N. Hoque	MIFS-ND: A mutual information-based feature selection method	2014	25	.pdf	application/pdf	7948	787	58	MIFS-ND: A Mutual Information-based Feature Selection Method Besides these, people use feature selection for dimensionality reduction and data minimization for learning, improving predictive accuracy, and increasing The main contribution of this paper is a mutual information-based feature subset selection [26] introduce a mutual information based feature selection method called mRMR (MaxRelevance and Min-Redundancy) that minimizes redundancy among features and maximizes dependency between a feature subset and a class label. [27] propose a mutual information-based feature selection method as a measure of relevance and redundancy among features. The proposed method uses mutual information theory to select a subset of relevant features. In this scenario, the proposed method selects the feature X1, since its feature-class mutual information is higher than X2. Hence, the method selects a feature that has higher feature-class mutual information as shown in The proposed algorithm first selects a subset of relevant features from each dataset.	./cache/work_oi5ldfwr25f2lm4vqqtv6sk5h4.pdf	./txt/work_oi5ldfwr25f2lm4vqqtv6sk5h4.txt