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Book synopsis

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.

Book details

Book category Books in English Computing & information technology Information technology: general issues

• Full title: Intelligent Data Engineering and Automated Learning
• Subtitle: Machine Learning from Imbalanced Data Sets
• Author: Rashid Syed Zahidur
• Language: English
• Binding: Paperback
• Number of pages: 220
• EAN: 9783639762211
• ISBN: 3639762215
• ID: 09216858
• Publisher: Scholars' Press
• Weight: 327 g
• Dimensions: 229 × 152 × 13 mm
• Date of publishing: 28. January 2015

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