An evolutionary multi-objective approach for prototype generation

TL;DR: Experimental results show that the proposed EMOPG method offers a better trade-off between accuracy and reduction than other methods, and a strategy for selecting a single solution from the set of non-dominated solutions is proposed.

Abstract: k-NN is one of the most popular and effective models for pattern classification. However, it has two main drawbacks that hinder the application of this method for large data sets: (1) the whole training set has to be stored in memory, and (2) for classifying a test pattern it has to be compared to all other training instances. In order to overcome these shortcomings, prototype generation (PG) methods aim to reduce the size of the training set while maintaining or increasing the classification performance of k-NN. Accordingly, most PG methods aim to generate instances that try to maximize classification performance. Nevertheless, in most cases, the reduction objective is only implicitly optimized. This paper introduces EMOPG, a novel approach to PG based on multi-objective optimization that explicitly optimizes both objectives: accuracy and reduction. Under EMOPG, prototypes are initialized with a subset of training instances selected through a tournament, according to a weighting term. A multi-objective evolutionary algorithm, PAES (Pareto Archived Evolution Strategy), is implemented to adjust the position of the initial prototypes. The optimization process aims to simultaneously maximize the classification performance of prototypes while reducing the number of instances with respect to the training set. A strategy for selecting a single solution from the set of non-dominated solutions is proposed. We evaluate the performance of EMOPG using a suite of benchmark data sets and compare the performance of our proposal with respect to the one obtained by alternative techniques. Experimental results show that our proposed method offers a better trade-off between accuracy and reduction than other methods.