RANDOM SUBSET FEATURE SELECTION FOR CLASSIFICATION

Abstract

Feature selection has been the focus of interest in the recent past. Large data sets are collected from scientific experiments and many times features are out numbered the observations.This demands for new approaches to minimize the data set without compromising the latent knowledge.This is also called dimensionality reduction. In this paper, we have presented a detailed review of methods used in minimizing the datasets. We have selected papers which are published last 10 years in the field of dimensionality reduction using Random Subset Feature Selection(RSFS). We have concentrated mainly on random subset feature selection methods used in the dimensionality reduction. The feature subset selection methods are classified into two 4 categories-Embedded, Filter, Wrapper and Hybrid. The data mining task flow from pre-processing, feature subset selection using random forest, random subset feature selection and classification. This survey is a comprehensive overview on random subset feature selection used in various applications.