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Variable Selection Method for Regression Models Using Computational Intelligence Techniques

Variable Selection Method for Regression Models Using Computational Intelligence Techniques
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Author(s): Dhamodharavadhani S. (Periyar University, India) and Rathipriya R. (Periyar University, India)
Copyright: 2020
Pages: 21
Source title: Handbook of Research on Machine and Deep Learning Applications for Cyber Security
Source Author(s)/Editor(s): Padmavathi Ganapathi (Avinashilingam Institute for Home Science and Higher Education for Women, India) and D. Shanmugapriya (Avinashilingam Institute for Home Science and Higher Education for Women, India)
DOI: 10.4018/978-1-5225-9611-0.ch019

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Abstract

Regression model (RM) is an important tool for modeling and analyzing data. It is one of the popular predictive modeling techniques which explore the relationship between a dependent (target) and independent (predictor) variables. The variable selection method is used to form a good and effective regression model. Many variable selection methods existing for regression model such as filter method, wrapper method, embedded methods, forward selection method, Backward Elimination methods, stepwise methods, and so on. In this chapter, computational intelligence-based variable selection method is discussed with respect to the regression model in cybersecurity. Generally, these regression models depend on the set of (predictor) variables. Therefore, variable selection methods are used to select the best subset of predictors from the entire set of variables. Genetic algorithm-based quick-reduct method is proposed to extract optimal predictor subset from the given data to form an optimal regression model.

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