9783330018464 - new method to improve mining of multi-class imbalanced data de al-roby, marwa; elhalees, alaa (5 resultados)
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: 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 majori…ty 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.
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Librería: moluna, Greven, Alemaniamoluna
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Condición: 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.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Taschenbuch. Condición: 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 c…lass 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.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: 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 cl…ass 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.
