A survey on feature selection methods

• We aim to provide a survey on feature selection methods with an introductory approach.
• We focus on various approaches and algorithms of feature selection rather than the applications of feature selection.
• We apply some of the algorithms to standard data sets to analyze and compare the feature selection algorithms.
• Feature Selection and Fault Analysis algorithms applied to RF generator data.

Plenty of feature selection methods are available in literature due to the availability of data with hundreds of variables leading to data with very high dimension. Feature selection methods provides us a way of reducing computation time, improving prediction performance, and a better understanding of the data in machine learning or pattern recognition applications. In this paper we provide an overview of some of the methods present in literature. The objective is to provide a generic introduction to variable elimination which can be applied to a wide array of machine learning problems. We focus on Filter, Wrapper and Embedded methods. We also apply some of the feature selection techniques on standard datasets to demonstrate the applicability of feature selection techniques.

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