Data as Voters: Core Set Selection Using Approval-Based Multi-Winner Voting

TL;DR

This paper introduces a novel data selection method for machine learning based on approval voting, where instances serve as both voters and candidates, leading to improved classifier performance.

Contribution

It proposes a new core set selection approach using approval-based multi-winner voting, integrating voting theory into data reduction for machine learning.

Findings

Improves classifier performance in several cases

Statistically significant improvements over state-of-the-art methods

Applicable to neural networks, KNN, and SVM classifiers

Abstract

We present a novel approach to the core set/instance selection problem in machine learning. Our approach is based on recent results on (proportional) representation in approval-based multi-winner elections. In our model, instances play a double role as voters and candidates. The approval set of each instance in the training set (acting as a voter) is defined from the concept of local set, which already exists in the literature. We then select the election winners by using a representative voting rule, and such winners are the data instances kept in the reduced training set. We evaluate our approach in two experiments involving neural network classifiers and classic machine learning classifiers (KNN and SVM). Our experiments show that, in several cases, our approach improves the performance of state-of-the-art methods, and the differences are statistically significant.