A gravitational density-based mass sharing method for imbalanced data classification

Abstract

Classification is one of the most popular branches of supervised learning algorithms. In the real-world problems, there are some situations in which distributions of the two classes are not the same. This situation is known as a class imbalanced problem. In the past years, several investigations have been done to find a way of handling imbalanced data, which most of them stay in one of two groups, including internal techniques and external techniques. The proposed gravitational density-based mass sharing method (GDMS) is an internal method that is designed based on k-nearest neighbor and fixed radius nearest neighbor (FRNN) rules. GDMS is a new technique that assigns masses to instances based on their local density while considering the global information too. In the labeling phase, GDMS decides based on the sum of gravitational forces coming from the candidates set, which are defined by FRNN rule. The GDMS does not need any parameters to be set in the whole procedure of classification, which is an advantage in comparison with the previous methods. To demonstrate the effectiveness of our proposed method, we use 40 standard datasets from the KEEL repository. The results show the effectiveness and superiority of GDMS compared to the competing methods.