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9783639762211: Intelligent Data Engineering and Automated Learning: Machine Learning from Imbalanced Data Sets

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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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  • EditoreVDM Verlag
  • Data di pubblicazione2015
  • ISBN 10 3639762215
  • ISBN 13 9783639762211
  • RilegaturaCopertina flessibile
  • LinguaInglese
  • Numero di pagine220
  • Contatto del produttorenon disponibile

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Syed Zahidur Rashid
Editore: Scholars\' Press, 2015
ISBN 10: 3639762215 ISBN 13: 9783639762211
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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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Syed Zahidur Rashid
Editore: Scholars' Press Jan 2015, 2015
ISBN 10: 3639762215 ISBN 13: 9783639762211
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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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Syed Zahidur Rashid
Editore: Scholars' Press, 2015
ISBN 10: 3639762215 ISBN 13: 9783639762211
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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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Syed Zahidur Rashid
Editore: 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. 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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