A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection

A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection

Feature selection is a task of choosing the best combination of potential features that best describes the target concept during a classification process. However, selecting such relevant features becomes a difficult matter when large number of features are involved. Therefore, this study aims to so...

Full description

Saved in:
Bibliographic Details
Main Authors: Too, Jing Wei, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah
Format: Article
Language:en
Published: MDPI Multidisciplinary Digital Publishing Institute 2019
Online Access:http://eprints.utem.edu.my/id/eprint/24623/2/2019%20A%20NEW%20CO-EVOLUTION%20BINARY%20PARTICLE%20SWARM%20OPTIMIZATION%20WITH%20MULTIPLE%20INERTIA%20WEIGHT%20STRATEGI%20FOR%20FEATURE%20SELECTION.PDF
http://eprints.utem.edu.my/id/eprint/24623/
https://www.mdpi.com/2227-9709/6/2/21/htm
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://eprints.utem.edu.my/id/eprint/24623/2/2019%20A%20NEW%20CO-EVOLUTION%20BINARY%20PARTICLE%20SWARM%20OPTIMIZATION%20WITH%20MULTIPLE%20INERTIA%20WEIGHT%20STRATEGI%20FOR%20FEATURE%20SELECTION.PDF
http://eprints.utem.edu.my/id/eprint/24623/
https://www.mdpi.com/2227-9709/6/2/21/htm

Similar Items