Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers
Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers
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DOI：
10.1016/j.patcog.2013.04.018
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发表时间：
2013-12-01
影响因子：
8
通讯作者：
Herrera, Francisco
中科院分区：
文献类型：
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作者：
Galar, Mikel;Fernandez, Alberto;Herrera, Francisco
一对一策略是克服多类分类问题最常用的分解技术之一；这样，考虑到原始问题中的类对，多类问题被划分为更容易解决的二元分类问题，然后由独立的基分类器学习。执行划分的方式产生了所谓的非能力。每当对实例进行分类时，都会出现此问题，因为它被提交给所有基分类器，尽管其中一些分类器的输出没有意义（它们没有使用要分类的实例类中的实例进行训练）。这个问题可能会导致错误的分类，因为尽管分类器不称职，但所有分类器的决策通常都会在聚合阶段考虑。在本文中，我们提出了一种一对一方案的动态分类器选择策略，试图在不称职的分类器的输出可能不感兴趣时避免使用不称职的分类器。我们考虑每个实例的邻域来决定分类器是否有效。为了验证所提出方法的有效性，我们将考虑不同的基分类器进行彻底的实验研究，并将我们的建议与所选五种机器学习范式中每个基分类器内最佳执行者最先进的聚合进行比较。实证分析得出的结果得到了适当统计分析的支持。 (C) 2013 Elsevier Ltd. 保留所有权利。
The One-vs-One strategy is one of the most commonly used decomposition technique to overcome multi-class classification problems; this way, multi-class problems are divided into easier-to-solve binary classification problems considering pairs of classes from the original problem, which are then learned by independent base classifiers.The way of performing the division produces the so-called non-competence. This problem occurs whenever an instance is classified, since it is submitted to all the base classifiers although the outputs of some of them are not meaningful (they were not trained using the instances from the class of the instance to be classified). This issue may lead to erroneous classifications, because in spite of their incompetence, all classifiers' decisions are usually considered in the aggregation phase.In this paper, we propose a dynamic classifier selection strategy for One-vs-One scheme that tries to avoid the non-competent classifiers when their output is probably not of interest. We consider the neighborhood of each instance to decide whether a classifier may be competent or not. In order to verify the validity of the proposed method, we will carry out a thorough experimental study considering different base classifiers and comparing our proposal with the best performer state-of-the-art aggregation within each base classifier from the five Machine Learning paradigms selected. The findings drawn from the empirical analysis are supported by the appropriate statistical analysis. (C) 2013 Elsevier Ltd. All rights reserved.