Hsu Kuo-Wei
Department of Computer Science, National Chengchi University, No. 64, Sec. 2, Zhi Nan Rd., Wen Shan District, Taipei City 11605, Taiwan.
Comput Intell Neurosci. 2017;2017:1930702. doi: 10.1155/2017/1930702. Epub 2017 Jan 31.
Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.
受群体决策过程的启发，人们发现分类器的集成或组合在广泛的应用领域中表现良好。一些研究人员提议使用两种不同类型的分类算法的混合来创建混合集成。这样的集成为何有效？这个问题仍然存在。遵循多样性的概念，多样性是集成成功的基本要素之一，我们对混合集成为何有效进行了理论分析，将使用不同算法与准确率提升联系起来。我们还对由决策树和朴素贝叶斯分类算法创建的分类器混合集成的分类性能进行了实验，这两种算法都是顶级数据挖掘算法，常用于创建非混合集成。因此，通过本文，我们为创建和使用混合集成的理论基础提供了补充。
