Optimal Linear Combination of Classifiers

TL;DR

This paper introduces a method to find the best linear combination of classifiers using a bias-variance framework, aiming to improve classification performance.

Contribution

It presents a novel approach for optimally combining classifiers linearly based on bias-variance analysis.

Findings

Demonstrates improved classification accuracy with the proposed combination method.

Provides a theoretical framework for optimal classifier combination.

Validates the approach through experiments on benchmark datasets.

Abstract

The question of whether to use one classifier or a combination of classifiers is a central topic in Machine Learning. We propose here a method for finding an optimal linear combination of classifiers derived from a bias-variance framework for the classification task.