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...

ver descrição completa

Detalhes bibliográficos

Principais autores:	Arfan Ali Nagra, Fei Han, Qing Hua Ling, Muhammad Abubaker, Farooq Ahmad, Sumet Mehta, Abeo Timothy Apasiba
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Taylor & Francis Group 2020-01-01
Colecção:	Connection Science
Assuntos:	classification feature selection adaptive particle swarm optimisation gradient base local search inertia weight
Acesso em linha:	http://dx.doi.org/10.1080/09540091.2019.1609419
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!