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A distance-based weighting framework for boosting the performance of dynamic ensemble selection

机译：基于距离的加权框架，可提高动态集合选择的性能

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摘要

Dynamic Ensemble Selection (DES) strategy is one of the most common and effective techniques in machine learning to deal with classification problems. DES systems aim to construct an ensemble consisting of the most appropriate classifiers selected from the candidate classifier pool according to the competence level of the individual classifier. Since several classifiers are selected, their combination becomes crucial. However, most of current DES approaches focus on the combination of the selected classifiers while ignoring the local information surrounding the query sample needed to be classified. In order to boost the performance of DES-based classification systems, we in this paper propose a dynamic weighting framework for the classifier fusion during obtaining the final output of an DES system. In particular, the proposed method first employs a DES approach to obtain a group of classifiers for a query sample. Then, the hypothesis vector of the selected ensemble is obtained based on the analysis of consensus. Finally, a distance-based weighting scheme is developed to adjust the hypothesis vector depending on the closeness of the query sample to each class. The proposed method is tested on 30 real-world datasets with six well-known DES approaches based on both homogeneous and heterogeneous ensemble. The obtained results, supported by proper statistical tests, show that our method outperforms, both in terms of accuracy and kappa measures, the original DES framework.
机译：动态集成选择（DES）策略是机器学习中用于处理分类问题的最常见，最有效的技术之一。 DES系统旨在构建一个集合，该集合由根据各个分类器的能力级别从候选分类器池中选择的最合适的分类器组成。由于选择了多个分类器，因此它们的组合至关重要。但是，当前的大多数DES方法都将重点放在所选分类器的组合上，而忽略了需要分类的查询样本周围的本地信息。为了提高基于DES的分类系统的性能，本文提出了一种动态加权框架，用于在获得DES系统的最终输出期间进行分类器融合。特别地，所提出的方法首先采用DES方法来获得一组查询样本的分类器。然后，基于共识分析，获得所选集合的假设向量。最后，开发了一种基于距离的加权方案，以根据查询样本与每个类别的接近程度来调整假设向量。在同构和异类集成的基础上，采用六种众所周知的DES方法在30个现实数据集上测试了该方法。在适当的统计测试的支持下，获得的结果表明，我们的方法在准确性和kappa度量方面均优于原始DES框架。

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