Ensemble Selection based on Classifier Prediction Confidence
作者：
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.
摘要
•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.
论文关键词：Ensemble method,Multiple classifier system,Ensemble selection,Classifier selection,Artificial bee colony
论文评审过程：Received 2 May 2019, Revised 27 September 2019, Accepted 3 November 2019, Available online 4 November 2019, Version of Record 8 November 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107104
