  Learning from many real-world datasets is limited by a problem called the
class imbalance problem. A dataset is imbalanced when one class (the majority
class) has significantly more samples than the other class (the minority
class). Such datasets cause typical machine learning algorithms to perform
poorly on the classification task. To overcome this issue, this paper proposes
a new approach Neighbors Progressive Competition (NPC) for classification of
imbalanced datasets. Whilst the proposed algorithm is inspired by weighted
k-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 of
nearest neighbors. In contrast, NPC considers progressively more neighbors of
the query sample in its decision making until the sum of grades for one class
is much higher than the other classes. Furthermore, NPC uses a novel method for
grading the training samples to compensate for the imbalance issue. The grades
are calculated using both local and global information. In brief, the
contribution of this paper is an entirely new classifier for handling the
imbalance issue effectively without any manually-set parameters or any need for
expert knowledge. Experimental results compare the proposed approach with five
representative algorithms applied to fifteen imbalanced datasets and illustrate
this algorithms effectiveness.
