Evidential Combination of Classifiers for Imbalanced Data

Abstract

It remains an important research topic for the classification of imbalanced data. There exist some methods to solve this problem, such as hybrid-sampling, over-sampling, and under-sampling. Each method has its own advantage, and different methods generally provide some complementary knowledge. We want to combine these three methods at the decision level in an appropriate way for achieving as good as possible classification performance. Evidence theory is expert at representing and combining uncertain information. So a new method called an evidential combination of classifiers (ECC) is proposed for dealing with imbalanced data. The classification result generated by different strategies (i.e., hybrid-sampling, over-sampling, or under-sampling) may have different reliabilities for query patterns. A cautious reliability evaluation rule is developed for each classification result based on the close neighborhoods. After that, the classification result is revised with a new belief redistribution way according to the reliability evaluation, and the probability/belief of one class can be partially transferred to other classes as well as the total ignorance, which is defined by the whole frame of classes. By doing this, we can reduce the error risk of each classification method. Then, the revised classification results from different methods are combined by evidence theory to make the final class decision. The effectiveness of the ECC method has been demonstrated using several experiments, and it shows that ECC can effectively improve the classification performance comparing with other related methods.