A survey of feature selection models for classification

B. Kalpana, Dr V. Saravanan, Dr. K. Vivekanandan

Abstract


The success of a machine learning algorithm depends on quality of data .The data given for classification, should not contain
irrelevant or redundant attributes. This increases the processing time. The data set, selected for classification should contain the right attributes
for accurate results. Feature selection is an essential data processing step, prior to applying a learning algorithm. Here we discuss some basic
feature selection models and evaluation function. Experimental results are compared for individual datasets with filter and wrapper model.

Keywords: Data mining , feature selection , filter model, wrapper model, classification


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DOI: https://doi.org/10.26483/ijarcs.v3i1.990

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