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Class imbalance is one of the challenging problems for data mining and machine learning techniques. The data in real-world applications often has imbalanced class distribution. That is occur when most examples are belong to a majority class and few example belong to a minority class. In this case, standard classifiers tend to classify all examples as a majority class and completely ignore the minority class. For this problem, researchers proposed a lot of solutions at both data and algorithmic levels. Most efforts concentrate on binary class problems. However, binary class is not the only scenario where the class imbalance problem prevails. In the case of multi-class data sets, it is much more difficult to define the majority and minority classes. Hence, multi class classification in imbalanced data sets remains an important topic of research. In our Book, we proposed new approach based on SOMTE (Synthetic Minority Over-sampling TEchnique) and clustering which is able to deal with imbalanced data problem involving multiple classes. We implemented our approach by using open source machine learning tools: Weka, and RapidMiner.
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Marwa F. Al-Roby, IT Lecturer, Holding a master degree in Information technology. Currently working as IT Lecturer in Saudi Arabia. Alaa M. ElHalees, Professor of Computer Science, Holding a PhD in Data mining, Currently working as Proff. at Faculty of Information Technology at Islamic University of Gaza.
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New Method to Improve Mining of Multi-Class Imbalanced Data
Verlag:
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330018461
ISBN 13: 9783330018464
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Zustand: New. 60 pp., paperback, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Bestandsnummer des Verkäufers ZB1315343
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New Method to Improve Mining of Multi-Class Imbalanced Data
Verlag:
LAP LAMBERT Academic Publishing Jan 2017, 2017
ISBN 10: 3330018461
ISBN 13: 9783330018464
Neu
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Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Class imbalance is one of the challenging problems for data mining and machine learning techniques. The data in real-world applications often has imbalanced class distribution. That is occur when most examples are belong to a majority class and few example belong to a minority class. In this case, standard classifiers tend to classify all examples as a majority class and completely ignore the minority class. For this problem, researchers proposed a lot of solutions at both data and algorithmic levels. Most efforts concentrate on binary class problems. However, binary class is not the only scenario where the class imbalance problem prevails. In the case of multi-class data sets, it is much more difficult to define the majority and minority classes. Hence, multi class classification in imbalanced data sets remains an important topic of research. In our Book, we proposed new approach based on SOMTE (Synthetic Minority Over-sampling TEchnique) and clustering which is able to deal with imbalanced data problem involving multiple classes. We implemented our approach by using open source machine learning tools: Weka, and RapidMiner. 80 pp. Englisch. Bestandsnummer des Verkäufers 9783330018464
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New Method to Improve Mining of Multi-Class Imbalanced Data
Verlag:
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330018461
ISBN 13: 9783330018464
Neu
Softcover
Anbieter: moluna, Greven, Deutschland
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Al-Roby MarwaMarwa F. Al-Roby, IT Lecturer, Holding a master degree in Information technology. Currently working as IT Lecturer in Saudi Arabia. Alaa M. ElHalees, Professor of Computer Science, Holding a PhD in Data mining, Currently. Bestandsnummer des Verkäufers 159137333
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New Method to Improve Mining of Multi-Class Imbalanced Data
Verlag:
LAP LAMBERT Academic Publishing Jan 2017, 2017
ISBN 10: 3330018461
ISBN 13: 9783330018464
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Class imbalance is one of the challenging problems for data mining and machine learning techniques. The data in real-world applications often has imbalanced class distribution. That is occur when most examples are belong to a majority class and few example belong to a minority class. In this case, standard classifiers tend to classify all examples as a majority class and completely ignore the minority class. For this problem, researchers proposed a lot of solutions at both data and algorithmic levels. Most efforts concentrate on binary class problems. However, binary class is not the only scenario where the class imbalance problem prevails. In the case of multi-class data sets, it is much more difficult to define the majority and minority classes. Hence, multi class classification in imbalanced data sets remains an important topic of research. In our Book, we proposed new approach based on SOMTE (Synthetic Minority Over-sampling TEchnique) and clustering which is able to deal with imbalanced data problem involving multiple classes. We implemented our approach by using open source machine learning tools: Weka, and RapidMiner.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch. Bestandsnummer des Verkäufers 9783330018464
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New Method to Improve Mining of Multi-Class Imbalanced Data
Verlag:
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330018461
ISBN 13: 9783330018464
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Class imbalance is one of the challenging problems for data mining and machine learning techniques. The data in real-world applications often has imbalanced class distribution. That is occur when most examples are belong to a majority class and few example belong to a minority class. In this case, standard classifiers tend to classify all examples as a majority class and completely ignore the minority class. For this problem, researchers proposed a lot of solutions at both data and algorithmic levels. Most efforts concentrate on binary class problems. However, binary class is not the only scenario where the class imbalance problem prevails. In the case of multi-class data sets, it is much more difficult to define the majority and minority classes. Hence, multi class classification in imbalanced data sets remains an important topic of research. In our Book, we proposed new approach based on SOMTE (Synthetic Minority Over-sampling TEchnique) and clustering which is able to deal with imbalanced data problem involving multiple classes. We implemented our approach by using open source machine learning tools: Weka, and RapidMiner. Bestandsnummer des Verkäufers 9783330018464
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EUR 35,90
EUR 60,69 Versand
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