Intelligent Data Engineering and Automated Learning

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.