A Density-Based Prototype Selection Approach

Abstract

Due to the increasing size of the datasets, prototype selection techniques have been applied for reducing the computational resources involved in data mining and machine learning tasks. In this paper, we propose a density-based approach for selecting prototypes. Firstly, it finds the density peaks in each dimension of the dataset. After that, it builds clusters of objects around these peaks. Finally, it extracts a prototype that represents each cluster and selects the most representative prototypes for including in the final reduced dataset. The proposed algorithm can deal with some crucial weak points of approaches that were previously proposed regarding the setting of parameters and the capability of dealing with high-dimensional datasets. Our method was evaluated on 14 well-known datasets used in a classification task. The performance of the proposed algorithm was compared to the performances of 8 prototype selection algorithms in terms of accuracy and reduction rate. The experimental results show that, in general, the proposed algorithm provides a good trade-off between reduction rate and the accuracy with reasonable time complexity.