When generating ensemble classifiers, selecting the best set of classifiers from the base classifier pool is considered a combinatorial problem and an efficient classifier selection methodology must be utilized. Different researchers have used different strategies such as evolutionary algorithms, genetic algorithms, rule-based algorithms, simulated annealing, and so forth to select the best set of classifiers that can maximize overall ensemble classifier accuracy. In this article, we present a novel classifier selection approach to generate an ensemble classifier. The proposed approach selects classifiers in multiple rounds of elimination. In each round, a classifier is given a chance to be selected to become a part of the ensemble, if it can contribute to the overall ensemble accuracy or diversity; otherwise, it is put back into the pool. Each classifier is given multiple opportunities to participate in rounds of selection and they are discarded only if they have no remaining chances. The process is repeated until no classifier in the pool has any chance left to participate in the round of selection. To test the efficacy of the proposed approach, 13 benchmark datasets from the UCI repository are used and results are compared with single classifier models and existing state-of-the-art ensemble classifier approaches. Statistical significance testing is conducted to further validate the results, and an analysis is provided.

中文翻译：

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使用准确性和多样性比较在集成学习中多次消除基分类器

在生成集成分类器时，从基本分类器池中选择最佳分类器集被认为是一个组合问题，必须使用有效的分类器选择方法。不同的研究人员使用了不同的策略，例如进化算法、遗传算法、基于规则的算法、模拟退火等，以选择可以最大化整体集成分类器精度的最佳分类器集。在本文中，我们提出了一种新的分类器选择方法来生成集成分类器。所提出的方法在多轮消除中选择分类器。在每一轮中，如果分类器有助于整体的准确性或多样性，则有机会被选择成为集成的一部分；否则，将其放回池中。每个分类器都有多个参与轮次选择的机会，只有在没有剩余机会时才会被丢弃。重复该过程，直到池中没有分类器有机会参与该轮选择。为了测试所提出方法的有效性，使用了来自 UCI 存储库的 13 个基准数据集，并将结果与​​单个分类器模型和现有的最先进的集成分类器方法进行了比较。进行统计显着性测试以进一步验证结果，并提供分析。为了测试所提出方法的有效性，使用了来自 UCI 存储库的 13 个基准数据集，并将结果与​​单个分类器模型和现有的最先进的集成分类器方法进行了比较。进行统计显着性测试以进一步验证结果，并提供分析。为了测试所提出方法的有效性，使用了来自 UCI 存储库的 13 个基准数据集，并将结果与​​单个分类器模型和现有的最先进的集成分类器方法进行了比较。进行统计显着性测试以进一步验证结果，并提供分析。