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Hybrid Methods in Feature Selection: A Data Classification Perspective - Senthamarai Kannan Subramanian
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ISBN: 9783844399240

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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. Buch / Broschur

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Subramanian, Senthamarai Kannan: Hybrid Methods in Feature Selection: A Data Classification Perspective
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Hybrid Feature Selection Methods are the proven methods for Large Scale Feature Selection Hybrid Feature Selection Methods are the proven methods for Large Scale Feature Selection Bücher > English, International > Gebundene Ausgaben

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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
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Subramanian, Senthamarai Kannan:
Hybrid Methods in Feature Selection: A Data Classification Perspective Hybrid Feature Selection Methods are the proven methods for Large Scale Feature Selection - neues Buch

2011, ISBN: 3844399240

ID: A14519517

Kartoniert / Broschiert, mit Schutzumschlag neu, [PU:LAP Lambert Acad. Publ.]

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Hybrid Methods in Feature Selection: A Data Classification Perspective - Subramanian, Senthamarai Kannan
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Subramanian, Senthamarai Kannan:
Hybrid Methods in Feature Selection: A Data Classification Perspective - Taschenbuch

2011, ISBN: 3844399240

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Hybrid Feature Selection Methods are the proven methods for Large Scale Feature Selection - Buch, gebundene Ausgabe, 64 S., Beilagen: Paperback, Erschienen: 2011 LAP Lambert Acad. Publ.

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Hybrid Methods in Feature Selection : A Data Classification Perspective - Subramanian, Senthamarai Kannan
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Hybrid Methods in Feature Selection : A Data Classification Perspective - neues Buch

2011, ISBN: 3844399240

ID: 8369470229

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Hybrid Methods in Feature Selection: A Data Classification Perspective

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.

Detailangaben zum Buch - Hybrid Methods in Feature Selection: A Data Classification Perspective


EAN (ISBN-13): 9783844399240
ISBN (ISBN-10): 3844399240
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2011
Herausgeber: LAP Lambert Acad. Publ.

Buch in der Datenbank seit 27.12.2009 08:40:43
Buch zuletzt gefunden am 17.11.2017 00:44:28
ISBN/EAN: 9783844399240

ISBN - alternative Schreibweisen:
3-8443-9924-0, 978-3-8443-9924-0


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