RANDOM SUBSET FEATURE SELECTION FOR CLASSIFICATION

Lakshmi Padmaja Dhyaram, Vishnuvardhan B

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.

Keywords


Terms—Feature Selection, Feature Subset Selection, Random Forests, Random Subset Feature Selection

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DOI: https://doi.org/10.26483/ijarcs.v9i2.5496

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