Ensemble Selection based on Classifier Prediction Confidence. (April 2020)

• Record Type: Journal Article
• Authors: Nguyen, Tien Thanh; Luong, Anh Vu; Dang, Manh Truong; Liew, Alan Wee-Chung; McCall, John
• Is Part Of: Pattern recognition. Volume 100(2020:Apr.)
• Publication Date: 2020-04
• Subjects: Ensemble method -- Multiple classifier system -- Ensemble selection -- Classifier selection -- Artificial bee colony; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.107104
• Languages: English
• ISSNs: 0031-3203

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. Abstract: Ensemble selection is one of the most studied topics in ensemble learning because a selected subset of base classifiers may perform better than the whole ensemble system. In recent years, a great many ensemble selection methods have been introduced. However, many of these lack flexibility: either a fixed subset of classifiers is pre-selected for all test samples (static approach), or the selection of classifiers depends upon the performance of techniques that define the region of competence (dynamic approach). In this paper, we propose an ensemble selection method that takes into account each base classifier's confidence during classification and the overall credibility of the base classifier in the ensemble. In other words, a base classifier is selected to predict for a test sample if the confidence in its prediction is higher than its credibility threshold. The credibility thresholds of the base classifiers are found by minimizing the empirical 0–1 loss on the entire training observations. In this way, our approach integrates both the static and dynamic aspects of ensemble selection. Experiments on 62 datasets demonstrate that the proposed method achieves much better performance in comparison to some ensemble methods.