Adaptive three-way KNN classifier using density-based granular balls

Abstract

The granular ball k-nearest neighbors (GBKNN) algorithm improves the efficiency and robustness of traditional k-nearest neighbors (KNN) by replacing point input with granular ball. However, in the generation process of granular balls by GBKNN, there may be unbalanced distribution of data points existed in some granular balls, which will cause more classification errors. In addition, the fixed value of k in GBKNN may also reduce the accuracy of classification. In order to address these issues, an adaptive three-way KNN classifier using density-based granular balls is proposed. Firstly, an improved density-based granular ball computing using density peak clustering is presented. This method introduces a refined threshold to subdivide the granular balls. Secondly, a data-driven neighborhood is defined to search the optimal k value and a density-based granular ball KNN (DBGBKNN) algorithm is proposed. Thirdly, by considering the fuzziness of the testing set in the classification process, the density-based granular ball KNN with three-way decision (DBGBKNN-3WD) is constructed. Finally, experimental results verify that DBGBKNN-3WD achieves high comprehensive score and low time complexity while maintaining less fuzziness loss than other algorithms.