Chi-Square Method of Feature Selection: Impact of Pre-Processing of Data

Authors

  • Alisha Sikri, N. P. Singh, Surjeet Dalal

Keywords:

essential, assumptions, Python, subjecting, Chi-Square, algorithms

Abstract

Feature selection is a technique of lowering computation and data collecting costs and rejecting the less significant or redundant factors/variable which in turn may also increase the efficiency of machine learning algorithms. One of the often-used method of feature selection for categorical data is Chi-Square method which is based on certain assumptions. Defilement of assumptions has an impact on the computed p-values which are surrogate to importance of the features. The main purpose of this study is to identify the impact of pre-processing of data keeping in view the assumptions of Chi-Square on raking of features. A secondary objective is to see how in-built sub-routines of computer languages such as Python or R are incorporating the assumptions of Chi-Square. Based on empirical evidence it was found that it is essential to pre-process the data to fulfill the assumptions of chi-square before subjecting it to analysis using either R or Python, or any other application available on web or otherwise.

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Author Biography

Alisha Sikri, N. P. Singh, Surjeet Dalal

Alisha Sikri1, N. P. Singh2, Surjeet Dalal3

1 Research Scholar, Department of Computer Science and Engineering, SRM University, Delhi-NCR, India, Email: alisha.sikri92@gmail.com
2 Professor, School of Business Management and Commerce, MVN University, (NCR), Palwal, Haryana, India. Email: dr.npsingh@mvn.edu.in
3 Professor, Department of Computer Science and Engineering, Amity University, Gurugram, Haryana, India. Email: sdalal@ggn.amity.edu

 

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Published

04.02.2023

How to Cite

Alisha Sikri, N. P. Singh, Surjeet Dalal. (2023). Chi-Square Method of Feature Selection: Impact of Pre-Processing of Data. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 241–248. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2680

Issue

Vol. 11 No. 3s (2023)

Section

Research Article

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Copyright (c) 2023 Alisha Sikri, N. P. Singh

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