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.

中文翻译：

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基于分类器预测置信度的集成选择

摘要 集成选择是集成学习中研究最多的主题之一，因为选定的基分类器子集可能比整个集成系统表现更好。近年来，引入了大量的集成选择方法。然而，其中许多缺乏灵活性：要么为所有测试样本预先选择一个固定的分类器子集（静态方法），要么分类器的选择取决于定义能力区域的技术的性能（动态方法）。在本文中，我们提出了一种集成选择方法，该方法考虑了每个基分类器在分类过程中的置信度以及集成中基分类器的整体可信度。换句话说，如果其预测的置信度高于其可信度阈值，则选择基分类器对测试样本进行预测。通过最小化整个训练观察的经验 0-1 损失来找到基分类器的可信度阈值。通过这种方式，我们的方法整合了集成选择的静态和动态方面。在 62 个数据集上的实验表明，与一些集成方法相比，所提出的方法实现了更好的性能。