论文标题
成本敏感的分层聚类用于动态分类器选择
Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection
论文作者
论文摘要
我们考虑动态分类器选择（DCS）问题：给定分类器的集合，我们将根据我们要分类的特定输入向量来选择要使用的分类器。这个问题是一般算法选择问题的特殊情况，在该问题中，我们可以使用多种不同的算法来处理给定的输入。我们研究了一种用于一般算法选择的方法，称为成本敏感的分层聚类（CSHC）适用于DC。我们为选择分类算法并评估其对性能的影响的特殊情况介绍了原始CSHC方法的一些补充。然后，我们将其与许多最新的动态分类器选择方法进行比较。我们的实验结果表明，我们修改的CSHC算法比较有利
We consider the dynamic classifier selection (DCS) problem: Given an ensemble of classifiers, we are to choose which classifier to use depending on the particular input vector that we get to classify. The problem is a special case of the general algorithm selection problem where we have multiple different algorithms we can employ to process a given input. We investigate if a method developed for general algorithm selection named cost-sensitive hierarchical clustering (CSHC) is suited for DCS. We introduce some additions to the original CSHC method for the special case of choosing a classification algorithm and evaluate their impact on performance. We then compare with a number of state-of-the-art dynamic classifier selection methods. Our experimental results show that our modified CSHC algorithm compares favorably
