Combining Multiple Algorithms in Classifier Ensembles using Generalized Mixture Functions

TL;DR

This paper introduces generalized mixture functions for classifier ensemble combination, utilizing dynamic weights to improve accuracy, and demonstrates their effectiveness through empirical analysis on multiple datasets.

Contribution

It proposes three novel GM functions for ensemble combination that adaptively assign dynamic weights, enhancing performance over traditional methods.

Findings

Performance gains over traditional combination methods

Comparable results with state-of-the-art ensemble techniques

Effective application across diverse datasets

Abstract

Classifier ensembles are pattern recognition structures composed of a set of classification algorithms (members), organized in a parallel way, and a combination method with the aim of increasing the classification accuracy of a classification system. In this study, we investigate the application of a generalized mixture (GM) functions as a new approach for providing an efficient combination procedure for these systems through the use of dynamic weights in the combination process. Therefore, we present three GM functions to be applied as a combination method. The main advantage of these functions is that they can define dynamic weights at the member outputs, making the combination process more efficient. In order to evaluate the feasibility of the proposed approach, an empirical analysis is conducted, applying classifier ensembles to 25 different classification data sets. In this analysis, we compare the use of the proposed approaches to ensembles using traditional combination methods as well as the state-of-the-art ensemble methods. Our findings indicated gains in terms of performance when comparing the proposed approaches to the traditional ones as well as comparable results with the state-of-the-art methods.