Self-paced learning for imbalanced data

Abstract

In this paper, we propose a novel training paradigm that combines two learning strategies: cost-sensitive and self-paced learning. This learning approach can be applied to the decision problems where highly imbalanced data is used during training process. The main idea behind the proposed method is to start the learning process by taking large number of minority examples and only the easiest majority objects and then gradually turning to more difficult cases. We examine the quality of this training paradigm comparing to other learning schemas for neural network model using a set of highly imbalanced benchmark datasets.