Does Score Bias Correction Improve the Fusion of Classifiers?

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.