Feature selection approach solves the dimensionality problem by removing irrelevant and redundant features. Recently, big data is widely available in information systems and data mining has pulled in a major thoughtfulness regarding analysts to transform such information into helpful learning. This implies the presence of low quality, questionable, excess and uproarious information which contrarily influence the way toward watching learning and helpful example. As follows, researchers require related big data utilizing feature selection methods. The process of feature selection is identifying the most relevant attributes and removing the redundant and irrelevant attributes. In this paper, find out the result of different feature selection methods based on a recognized dataset (i.e., gens expression dataset) and classification algorithms were used to evaluate the performance of the algorithms. In this study revealed that feature selection methods are capable to improve the performance of learning algorithms. Still, there are no any single filter based feature selection method is the best. Taken as a whole, Classifier AttEval, CorrelationAttributeEval, Principal Components, and ReliefAttEval methods performed better results than the others.

Feature selection, Lung cancer, Gens expression, Classifier, Subset

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