Addressing the Curse of Imbalanced Training Sets: One-Sided Selection

Adding examples of the majority class to the training set can have a detrimental eeect on the learner's behavior: noisy or otherwise unreliable examples from the majority class can overwhelm the minority class. The paper discusses criteria to evaluate the utility of clas-siiers induced from such imbalanced training sets, gives explanation of the poor behavior of some learners under these circumstances, and suggests as a solution a simple technique called one-sided selection of examples.