An attraction-based approach for instance selection

Abstract

Due to the increasing size of the datasets, instance selection techniques have been applied for reducing the computational resources involved in data mining and machine learning tasks. In this paper, we propose an attraction-based approach for selecting instances. The algorithm adopts the notion of attraction for selecting the most representative instances of each class. The resulting approach allows the user to define how many representative instances should be selected. Our method was evaluated in a classification task considering 14 well-known datasets. 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 accuracy with a low time complexity.