Ensemble Selection based on Classifier Prediction Confidence

Tien Thanh Nguyen, Anh Vu Luong, Manh Truong Dang, Alan Wee-Chung Liew, John McCall

Abstract: Highlights•An ensemble selection method that takes into account each base classifier's confidence during classification and its overall credibility on the task is proposed.•The overall credibility of a base classifier is obtained by minimizing the empirical 0–1 loss on the entire training set.•The classifier's confidence in prediction for a test sample is measured by the entropy of its soft classification outputs for that sample.•Extensive comparative experiments with the state-of-the-art algorithms on ensemble selection validated the superior performance of our algorithm.