Classifier ensemble creation via false labelling

TL;DR

This paper introduces a novel ensemble creation method that automatically generates optimal labels for classifiers, leading to improved performance over individual classifiers on biomedical datasets.

Contribution

The paper presents a new automatic label generation approach for classifier ensembles, differing from existing methods that rely on classifier selection or data preprocessing.

Findings

Outperformed individual classifiers on biomedical datasets

Effective in high-dimensional data scenarios

Demonstrated consistent improvement across all tested cases

Abstract

In this paper, a novel approach to classifier ensemble creation is presented. While other ensemble creation techniques are based on careful selection of existing classifiers or preprocessing of the data, the presented approach automatically creates an optimal labelling for a number of classifiers, which are then assigned to the original data instances and fed to classifiers. The approach has been evaluated on high-dimensional biomedical datasets. The results show that the approach outperformed individual approaches in all cases.