Dissimilarity-based learning from imbalanced data with small disjuncts and noise

Abstract

This papers compares the behavior of three linear classifiers modeled on both the feature space and the dissimilarity space when the class imbalance of data sets interweaves with small disjuncts and noise. To this end, experiments are carried out over three synthetic databases with different imbalance ratios, levels of noise and complexity of the small disjuncts. Results suggest that small disjuncts can be much better overcome on the dissimilarity space than on the feature space, which means that the learning models will be only affected by imbalance and noise if the samples have firstly been mapped into the dissimilarity space.