Proposing new method to improve gravitational fixed nearest neighbor algorithm for imbalanced data classification

Abstract

Classification of imbalanced data sets is one of the basic challenges in the field of machine learning and data mining. There have been many proposed methods for classification of such data sets up to now. In algorithmic level methods, new algorithms are created which are adapted to the nature of imbalanced data sets. Gravitational fixed radius nearest neighbor algorithm (GFRNN) is an algorithmic level method developed with the aim of enhancing k nearest neighbor classifier to acquire the ability of dealing with imbalanced data sets. This algorithm, utilizes the sum of gravitational forces on a query sample from its nearest neighbors in a fixed radius to determine its label. Simplicity and no need for parameter setting during the run of algorithm are the main advantages of this method. In this paper, a method is proposed for improving the performance of GFRNN algorithm in which mass assigning of each training sample is done based on the sum of gravitational forces from other training samples on it. The results obtained from applying the proposed method on 10 data sets prove the superiority of it compared with 5 other algorithms.