Feature selection using firefly optimization for classification and regression models

doi:10.1016/j.dss.2017.12.001

Feature selection using firefly optimization for classification and regression models

2018-02-01

91

OA

AI

DOI：10.1016/j.dss.2017.12.001

Original

Save

View PDF

Abstract

En 中文

In this research, we propose a variant of the Firefly Algorithm (FA) for discriminative feature selection in classification and regression models for supporting decision making processes using data-based learning methods. The FA variant employs Simulated Annealing (SA)-enhanced local and global promising solutions, chaotic accelerated attractiveness parameters and diversion mechanisms of weak solutions to escape from the local optimum trap and mitigate the premature convergence problem in the original FA algorithm. A total of 29 classification and 11 regression benchmark data sets have been used to evaluate the efficiency of the proposed FA model. It shows statistically significant improvements over other state-of-the-art FA variants and classical search methods for diverse feature selection problems. In short, the proposed FA variant offers an effective method to identify optimal feature subsets in classification and regression models for supporting data-based decision making processes. (C) 2017 Elsevier B.V. All rights reserved.

Keywords:

Feature selection

Dimensionality reduction

Classification

Regression

Firefly algorithm

Feature selection using firefly optimization for classification and regression models

Abstract

AI Summary

Journal

Organization