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A Meta Classifier by Clustering of Classifiers

机译:通过分类器聚类的元分类器

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To learn any problem, many classifiers have been introduced so far. Each of these classifiers has many strengths (positive aspects) and weaknesses (negative aspects) that make it suitable for some specific problems. But there is no powerful solution to indicate which classifier is the best classifier (or at least a good one) for a special problem. Fortunately the ensemble learning provides us with a powerful approach to prepare a near-to-optimum classifying system for any given problem. How to create a suitable ensemble of base classifiers is the most challenging problem in classifier ensemble. An ensemble vitally needs diversity. It means that if a pool of classifiers wants to be successful as an ensemble, they must be diverse enough to cover the errors of each other. So during creation of an ensemble, we need a mechanism to guarantee the ensemble classifiers are diversity. Sometimes this mechanism is to select/remove a subset of the produced base classifiers with the aim of maintaining the diversity among the ensemble. This paper proposes an innovative ensemble creation named the Classifier Selection Based on Clustering (CSBC). The CSBC guarantees the necessary diversity among ensemble classifiers, using the clustering of classifiers technique. It uses bagging as generator of the base classifiers. After producing a large number of the base classifiers, CSBC partitions them using a clustering algorithm. After that by selecting one classifier from each cluster, CSBC produces the final ensemble. The weighted majority vote method is taken as aggregator function of the ensemble. Here it is probed how the cluster number affects the performance of the CSBC method and how we can choose a good approximate value for cluster number in any dataset adaptively. We expand our studies on a large number of real datasets of UCI repository to reach a decisive conclusion.
机译:为了学习任何问题,到目前为止已经引入了许多分类器。这些分类器中的每一个都有很多优点(积极方面)和缺点(不利方面),使其适用于某些特定问题。但是,没有强大的解决方案来指出哪个分类器是针对特定问题的最佳分类器(或至少是一个好的分类器)。幸运的是,集成学习为我们提供了一种强大的方法,可以为任何给定问题准备接近最佳的分类系统。如何创建合适的基础分类器集合是分类器集合中最具挑战性的问题。一个合奏团至关重要地需要多样性。这意味着,如果一个分类器池想要作为一个整体而成功,则它们必须足够多样化以覆盖彼此的错误。因此,在创建整体时,我们需要一种机制来确保整体分类器具有多样性。有时,此机制是选择/删除生成的基本分类器的子集,目的是保持整体之间的多样性。本文提出了一种创新的集合创建方法,称为基于聚类的分类器选择(CSBC)。 CSBC使用分类器的聚类技术保证集合分类器之间必要的多样性。它使用装袋法作为基本分类器的生成器。生成大量基本分类器后,CSBC使用聚类算法对它们进行分区。之后,通过从每个聚类中选择一个分类器,CSBC产生最终的合奏。加权多数投票法被认为是集合的聚合函数。这里探讨了簇号如何影响CSBC方法的性能,以及我们如何在任何数据集中自适应地为簇号选择一个好的近似值。我们将研究扩展到大量UCI知识库的真实数据集,以得出决定性的结论。

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