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Learning from many real-world datasets is limited by a problem called theclass imbalance problem. A dataset is imbalanced when one class (the majorityclass) has significantly more samples than the other class (the minorityclass). Such datasets cause typical machine learning algorithms to performpoorly on the classification task. To overcome this issue, this paper proposesa new approach Neighbors Progressive Competition (NPC) for classification ofimbalanced datasets. Whilst the proposed algorithm is inspired by weightedk-Nearest Neighbor (k-NN) algorithms, it has major differences from them.Unlike k- NN, NPC does not limit its decision criteria to a preset number ofnearest neighbors. In contrast, NPC considers progressively more neighbors ofthe query sample in its decision making until the sum of grades for one classis much higher than the other classes. Furthermore, NPC uses a novel method forgrading the training samples to compensate for the imbalance issue. The gradesare calculated using both local and global information. In brief, thecontribution of this paper is an entirely new classifier for handling theimbalance issue effectively without any manually-set parameters or any need forexpert knowledge. Experimental results compare the proposed approach with fiverepresentative algorithms applied to fifteen imbalanced datasets and illustratethis algorithms effectiveness.

NPC: Neighbors Progressive Competition Algorithm for Classification of Imbalanced Data Sets