Work

Does Score Bias Correction Improve the Fusion of Classifiers?

Year: 2025

Type: article

Abstract: We demonstrate that the potential bias in the scores generated by individual classifiers negatively affects their fusion. Consequently, we present an algorithm to improve the effectiveness of score fusion in classification. The algorithm corrects the score class conditional bias before fusion. The interest of the procedure is demonstrated theoretically, first in general terms and then considering exponential models for the score class conditional distributions. The case of beta distributions is also addressed using Monte Carlo simulations. Finally, a real-life application of fusion of two modalities (EEG, ECG) and two classifiers (Gaussian Bayes and Logistic Regression) is included, showing significant improvement with respect to conventional fusion without bias correction.

Source: Machine Learning and Knowledge Extraction

DOI: 10.3390/make7040151

Authors: Luis Vergara, Addisson Salazar

Institution: Universitat Politècnica de València

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funders: Generalitat Valenciana, Agencia Estatal de Investigación