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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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

Dati bibliografici

• Titolo: Intelligent Data Engineering and Automated Learning : Machine Learning from Imbalanced Data Sets
• Editore: Scholars' Press
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 3639762215
• ISBN 13: 9783639762211
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 150 A x 14 P (millimetri) 220 millimetri di larghezza per 150 millimetri di altezza per 14 millimetri di profondità
• Peso: 346 grammi

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Riassunto

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.