id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_v747xjw75jfu5drznwrk6szweu	Georgina Stegmayer	Automatic recognition of quarantine citrus diseases	2013	27	.pdf	application/pdf	9443	3163	88	Pattern recognition, multiclass classification, neural networks, citrus diseases This paper presents a classifier able to distinguish among the three quarantine diseases mentioned, based on a binary description of the presence diseases recognition, which includes a feature selection step and a classifier model selection; data set 2 (DS2) having the remaining 75% (141 samples) Feature or attribute selection is an active research area in pattern recognition, statistics, and data mining. little or no predictive information and select only a subset of relevant features for building robust learning models. Classifiers training and testing on selected features After the feature and model selection steps, the classifiers were trained The proposed approach is based on the combination of a feature selection method and a classifier that has been trained on the illness symptoms. The high classification rates that have been obtained on the task of automatic recognition of quarantine citrus diseases show the usefulness of the	./cache/work_v747xjw75jfu5drznwrk6szweu.pdf	./txt/work_v747xjw75jfu5drznwrk6szweu.txt