nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

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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: Taschenbuch
Condition: Neu
Dimensions: 220 millimeters width by 150 millimeters height by 33 millimeters depth
Weight: 131 grams

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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.

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