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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Maximum relevancy maximum complementary based ordered aggregation for ensemble pruning
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Maximum relevancy maximum complementary based ordered aggregation for ensemble pruning

机译：基于最大相关性的基于互补聚集的集合修剪

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摘要

Ensemble methods have delivered exceptional performance in various applications. However, this exceptional performance is achieved at the expense of heavy storage requirements and slower predictions. Ensemble pruning aims at reducing the complexity of this popular learning paradigm without worsening its performance. This paper presents an efficient and effective ordering-based ensemble pruning methods which ranks all the base classifiers with respect to a maximum relevancy maximum complementary (MRMC) measure. The MRMC measure evaluates the base classifier's classification ability as well as its complementariness to the ensemble, and thereby a set of accurate and complementary base classifiers can be selected. Moreover, an evaluation function that deliberately favors the candidate sub-ensembles with a better performance in classifying low margin instances has also been proposed. Experiments performed on 25 benchmark datasets demonstrate the effectiveness of our proposed method.
机译：集合方法在各种应用中提供了卓越的性能。 但是，这种特殊的性能是以重大存储要求和更慢的预测来实现的。 合奏修剪旨在减少这种流行的学习范例的复杂性而不会恶化其性能。 本文介绍了一种高效且有效的顺序排列的精炼方法，其对所有基本分类器相对于最大相关性最大互补（MRMC）测量等级。 MRMC措施评估基本分类器的分类能力，以及它对合奏的互补性，从而可以选择一组准确和互补的基础分类器。 此外，还提出了一种评估函数，刻意有意利用在分类低保证金实例中具有更好性能的候选子组合。 在25个基准数据集上执行的实验证明了我们提出的方法的有效性。

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