Ensemble of Binary Classifiers Combined Using Recurrent Correlation Associative Memories

TL;DR

This paper introduces a novel ensemble classification method using recurrent correlation associative memories (RCAMs), which adaptively combine base classifiers based on their similarity, showing promising results in binary classification tasks.

Contribution

The paper proposes a new ensemble approach that leverages RCAMs to dynamically weight classifiers based on similarity, extending traditional voting schemes.

Findings

RCAM-based ensemble acts as a weighted majority vote

Experimental results confirm effectiveness in binary classification

Method shows potential for improved ensemble performance

Abstract

An ensemble method should cleverly combine a group of base classifiers to yield an improved classifier. The majority vote is an example of a methodology used to combine classifiers in an ensemble method. In this paper, we propose to combine classifiers using an associative memory model. Precisely, we introduce ensemble methods based on recurrent correlation associative memories (RCAMs) for binary classification problems. We show that an RCAM-based ensemble classifier can be viewed as a majority vote classifier whose weights depend on the similarity between the base classifiers and the resulting ensemble method. More precisely, the RCAM-based ensemble combines the classifiers using a recurrent consult and vote scheme. Furthermore, computational experiments confirm the potential application of the RCAM-based ensemble method for binary classification problems.