A SURVEY OF FEATURE SELECTION MODELS FOR COMPONENT BASED SYSTEMS

Abstract

A lot of feature selection strategies are accessible in writing because of the accessibility of information with several variables prompting information with high
measurement. Feature selection strategies give us a method for lessening calculation time, enhancing expectation execution, and a superior comprehension of the information in machine learning or example  acknowledgment applications. In this paper we give an outline of a percentage of the strategies present in writing. The goal is to give a nonexclusive prologue to variable end which can be connected to a wide exhibit of machine learning issues. We concentrate on
Filter, Wrapper and Embedded systems. We likewise apply a portion of the feature selection methods on standard datasets to show the appropriateness of feature
selection strategies.