An empirical study of binary classifier fusion methods for multiclass classification
An empirical study of binary classifier fusion methods for multiclass classification
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DOI：
10.1016/j.inffus.2010.06.010
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发表时间：
2011-04
期刊：
影响因子：
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通讯作者：
Nicolás E. García-Pedrajas;Domingo Ortiz-Boyer
中科院分区：
文献类型：
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作者：
Nicolás E. García-Pedrajas;Domingo Ortiz-Boyer
多分类器系统中单个分类器的组合是信息融合研究的重要课题之一。在这方面，我们有两个不同的任务：一个是训练和构建分类器集合，每个分类器都能够解决多类问题;另一个任务是融合二进制分类器，每个分类器解决不同的两类问题，以构建多类分类器。本文主要研究了二值分类器融合过程中的几个方面，以获得一个多类分类器。在一般情况下的分类问题有两个以上的类，我们面临的问题，许多算法要么工作更好地与两类问题，或专门设计的两类问题。在这种情况下，必须使用将多类问题映射为几个两类问题的二值化方法。在这项任务中，信息融合起着核心作用，因为不同的二进制分类器的预测到一个多类分类器的组合。这个任务提出了关于二进制学习器的训练和组合方式的几个问题。个体准确性、多样性和独立性等问题对于其他信息融合任务（如分类器集合的构建）是常见的。本文提出了一种研究的不同类别的二值化方法的各种标准的多类分类问题，同时解决在以前的作品中没有考虑的方面。我们特别关注该领域中许多尚未通过实验充分评估的一般假设。我们在UCI机器学习库中的大量现实问题中测试了不同的方法，我们使用了六种不同的基础学习器。我们的研究结果证实了文献中的一些先前的结果。此外，我们提出了新的结果的基础学习者对每种方法的性能的影响。我们还展示了新的结果的行为的二进制测试错误和独立的二进制分类器取决于编码策略。最后，我们研究的行为的方法时，类的数量是高的，在存在噪声。
One of the most important topics in information fusion is the combination of individual classifiers in multi-classifier systems. We have two different tasks in this area: one is the training and construction of ensembles of classifiers, with each one being able to solve the multiclass problem; the other task is the fusion of binary classifiers, with each one solving a different two-class problem to construct a multiclass classifier. This paper is devoted to the study of several aspects on the fusion process of binary classifiers to obtain a multiclass classifier. In the general case of a classification problem with more than two classes, we are faced with the issue that many algorithms either work better with two-class problems or are specifically designed for two-class problems. In such cases, a binarization method that maps the multiclass problem into several two-class problems must be used. In this task, information fusion plays a central role because of the combination of the prediction of the different binary classifiers into a multiclass classifier. Several issues regarding the way binary learners are trained and combined are raised by this task. Issues such as individual accuracy, diversity, and independence are common to other information fusion tasks such as the construction of ensembles of classifiers. This paper presents a study of the different class binarization methods for the various standard multiclass classification problems that have been proposed while addressing aspects not considered in previous works. We are especially concerned with many of the general assumptions in the field that have not been fully assessed by experimentation. We test the different methods in a large set of real-world problems from the UCI Machine Learning Repository, and we use six different base learners. Our results corroborate some of the previous results present in the literature. Furthermore, we present new results regarding the influence of the base learner on the performance of each method. We also show new results on the behavior of binary testing error and the independence of binary classifiers depending on the coding strategy. Finally, we study the behavior of the methods when the number of classes is high and in the presence of noise.