A quantum particle swarm optimization approach for feature selection: Metaheuristics in Data classification, Datamining

Sihem, Benkhaled

ISBN 10: 6200284431 / ISBN 13: 9786200284433

Published by LAP LAMBERT Academic Publishing, 2019

About this Item

76 pages. 8.66x5.91x0.18 inches. In Stock.

Seller Inventory # zk6200284431

Bibliographic Details

• Title: A quantum particle swarm optimization approach for feature selection: Metaheuristics in Data classification, Datamining
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 6200284431
• ISBN 13: 9786200284433
• Binding: Paperback
• Condition: Brand New
• Weight: 0.16 kilograms

About this title

Synopsis

This book represent a feature selection approach. Feature selection is a pre-processing step that plays an important role in data mining, allowing it to search a reduced subset of features from a large set of features by eliminating redundant and irrelevant features for performing the supervised classification task, all trying to maintain or improve classifier performance. The search for a subset of features is an NP-difficult optimization problem that can be solved by metaheuristics; we have been interested in the metaheuristics resulting from the intelligence in swarms for the features selection.