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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Autors principals:	Arfan Ali Nagra, Fei Han, Qing Hua Ling, Muhammad Abubaker, Farooq Ahmad, Sumet Mehta, Abeo Timothy Apasiba
Format:	Artigo
Idioma:	Inglês
Publicat:	Taylor & Francis Group 2020-01-01
Col·lecció:	Connection Science
Matèries:	classification feature selection adaptive particle swarm optimisation gradient base local search inertia weight
Accés en línia:	http://dx.doi.org/10.1080/09540091.2019.1609419
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