Intelligent Data Engineering and Automated Learning | Machine Learning from Imbalanced Data Sets
Syed Zahidur Rashid
ISBN 10: 3639762215 ISBN 13: 9783639762211
Editore: Scholars' Press, 2015
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Intelligent Data Engineering and Automated Learning | Machine Learning from Imbalanced Data Sets | Syed Zahidur Rashid | Taschenbuch | Englisch | 2015 | Scholars' Press | EAN 9783639762211 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 113176209
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This book investigates the nature of imbalanced data sets and looks at two external methods, which can increase a learner's performance on under represented classes. Both techniques artificially balance the training data; one by randomly re-sampling examples of the under represented class and adding them to the training set, the other by randomly removing examples of the over represented class from the training set. A combination scheme is then presented. The approach is one in which multiple classifiers are arranged in a hierarchical structure according to their sampling techniques. The architecture consists of two experts, one that boosts performance by combining classifiers that re-sample training data at different rates, the other by combining classifiers that remove data from the training data at different rates. Using the F-measure, which combines precision and recall as a performance statistic, the combination scheme is shown to be effective at learning from severely imbalanced data sets. In fact, when compared to a state of the art combination technique, Adaptive-Boosting, the proposed system is shown to be superior for learning on imbalanced data sets.
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Titolo: Intelligent Data Engineering and Automated ...
Casa editrice: Scholars' Press
Data di pubblicazione: 2015
Legatura: Taschenbuch
Condizione: Neu
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Intelligent Data Engineering and Automated Learning
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Rashid Syed ZahidurThe author s research interests are in the areas of machine learning, data mining, information acquisition, and decision theory. Specifically, in active learning, active inference, interactive machine learning, sta. Codice articolo 151400880
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Intelligent Data Engineering and Automated Learning
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Scholars' Press Jan 2015, 2015
ISBN 10: 3639762215
ISBN 13: 9783639762211
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book investigates the nature of imbalanced data sets and looks at two external methods, which can increase a learner s performance on under represented classes. Both techniques artificially balance the training data; one by randomly re-sampling examples of the under represented class and adding them to the training set, the other by randomly removing examples of the over represented class from the training set. A combination scheme is then presented. The approach is one in which multiple classifiers are arranged in a hierarchical structure according to their sampling techniques. The architecture consists of two experts, one that boosts performance by combining classifiers that re-sample training data at different rates, the other by combining classifiers that remove data from the training data at different rates. Using the F-measure, which combines precision and recall as a performance statistic, the combination scheme is shown to be effective at learning from severely imbalanced data sets. In fact, when compared to a state of the art combination technique, Adaptive-Boosting, the proposed system is shown to be superior for learning on imbalanced data sets. 220 pp. Englisch. Codice articolo 9783639762211
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Intelligent Data Engineering and Automated Learning : Machine Learning from Imbalanced Data Sets
Print on DemandDa: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book investigates the nature of imbalanced data sets and looks at two external methods, which can increase a learner s performance on under represented classes. Both techniques artificially balance the training data; one by randomly re-sampling examples of the under represented class and adding them to the training set, the other by randomly removing examples of the over represented class from the training set. A combination scheme is then presented. The approach is one in which multiple classifiers are arranged in a hierarchical structure according to their sampling techniques. The architecture consists of two experts, one that boosts performance by combining classifiers that re-sample training data at different rates, the other by combining classifiers that remove data from the training data at different rates. Using the F-measure, which combines precision and recall as a performance statistic, the combination scheme is shown to be effective at learning from severely imbalanced data sets. In fact, when compared to a state of the art combination technique, Adaptive-Boosting, the proposed system is shown to be superior for learning on imbalanced data sets. Codice articolo 9783639762211
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Intelligent Data Engineering and Automated Learning
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Scholars' Press Jan 2015, 2015
ISBN 10: 3639762215
ISBN 13: 9783639762211
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book investigates the nature of imbalanced data sets and looks at two external methods, which can increase a learner¿s performance on under represented classes. Both techniques artificially balance the training data; one by randomly re-sampling examples of the under represented class and adding them to the training set, the other by randomly removing examples of the over represented class from the training set. A combination scheme is then presented. The approach is one in which multiple classifiers are arranged in a hierarchical structure according to their sampling techniques. The architecture consists of two experts, one that boosts performance by combining classifiers that re-sample training data at different rates, the other by combining classifiers that remove data from the training data at different rates. Using the F-measure, which combines precision and recall as a performance statistic, the combination scheme is shown to be effective at learning from severely imbalanced data sets. In fact, when compared to a state of the art combination technique, Adaptive-Boosting, the proposed system is shown to be superior for learning on imbalanced data sets.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 220 pp. Englisch. Codice articolo 9783639762211
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