A Multi-Objective Particle Swarm Optimization For Feature Selection

Language: English

Published by LAP LAMBERT Academic Publishing Jul 2018, 2018

6139864631 / 9786139864638

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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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This item is printed on demand - it takes 3-4 days longer - Neuware -Feature selection is very important task in classification. Number of features is available for classification but not all of them are useful. Irrelevant and redundant features may even reduce the performance. There are two types of feature selection approaches. They are wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm when searching the goodness of the features during the feature selection process while filters are independent of any classification algorithm. The goal of Feature selection is to choose a small number of relevant features to achieve similar or even better classification performance than using all features. Existing feature selection algorithms treat the task as a single objective problem. The proposed system can treat as a multi objective problem. It has two objectives. They are maximizing the classification performance and minimizing the number of features. The proposed system is PSO-based multi-objective feature selection algorithm. The algorithm (NSPSOFS) introduces the task is to generate a Pareto front of non dominated solutions idea of non dominated sorting into PSO to address feature selection problems. 60 pp. Englisch.

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Title
A Multi-Objective Particle Swarm Optimization For Feature Selection
Author
Y. Mohana Roopa
Publisher
LAP LAMBERT Academic Publishing Jul 2018
Publication year
2018
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6139864631
ISBN 13
9786139864638
Item weight
107 grams
Dimensions
220x150x4 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

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BuchWeltWeit Ludwig Meier e.K.

Germany