Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisati...

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Bibliographic Details
Main Authors: Arfan Ali Nagra, Fei Han, Qing Hua Ling, Muhammad Abubaker, Farooq Ahmad, Sumet Mehta, Abeo Timothy Apasiba
Format: Article
Language:English
Published: Taylor & Francis Group 2020-01-01
Series:Connection Science
Subjects:
classification
feature selection
adaptive particle swarm optimisation
gradient base local search
inertia weight
Online Access:http://dx.doi.org/10.1080/09540091.2019.1609419
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http://dx.doi.org/10.1080/09540091.2019.1609419

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