Hybrid Methods in Feature Selection: A Data Classification Perspective: Hybrid Feature Selection Methods are the proven methods for Large Scale Feature Selection

Subramanian, Senthamarai Kannan

ISBN 10: 3844399240 / ISBN 13: 9783844399240

Published by LAP LAMBERT Academic Publishing, 2011

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Bibliographic Details

• Title: Hybrid Methods in Feature Selection: A Data Classification Perspective: Hybrid Feature Selection Methods are the proven methods for Large Scale Feature Selection
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3844399240
• ISBN 13: 9783844399240
• Binding: Paperback
• Condition: Like New
• Book Type: book

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Synopsis

In recent years, data have become increasingly larger in both number of instances and number of features in many applications. This enormity may cause serious problems to many machine learning algorithms with respect to scalability and learning performance. Therefore, feature selection is essential for the machine learning algorithms while handling high dimensional datasets. Many traditional search methods have shown promising results in a number of feature selection problems. However, as the number of features increases extremely, most of these existing methods face the problem of intractable computational time. Since no single feature selection method could handle all requirements of feature selection in real world datasets, hybrid methods prsented here are the tested methods for effecive Feature Selection.One viable option is to apply a ranking feature selection method to obtain a manageable number of top ranked features which could be further handled by traditional feature selection methods for further analysis.

About the Author

Dr S.SENTHAMARAI KANNAN is presently working as Assistant Professor with Thiagarajar College of Engineering , India. He has published nine papers in reputed international journals including Elsevier Knowledge Based Systems and the CODATA Data Science Journal and he has been cited by many as the authority in the field of Feature Selection.