Cost-sensitive Bayesian network classifiers
Cost-sensitive Bayesian network classifiers
复制标题
成本敏感的贝叶斯网络分类器
DOI：
10.1016/j.patrec.2014.04.017
复制
发表时间：
2014-08-01
影响因子：
5.1
通讯作者：
Wang, Shasha
中科院分区：
文献类型：
--
作者：
Jiang, Liangxiao;Li, Chaoqun;Wang, Shasha
近年来，对成本敏感的学习受到越来越多的关注。然而，在现有的研究中，大部分的工作都致力于决策树成本敏感和成本敏感的贝叶斯网络分类器的工作很少讨论。在本文中，实例加权方法被纳入各种贝叶斯网络分类器。通过实例加权方法对贝叶斯网络分类器的概率估计进行修正，使贝叶斯网络分类器具有代价敏感性。在36个UCI数据集上的实验结果表明，当代价比较大时，代价敏感贝叶斯网络分类器在总误分类代价和高代价错误数方面表现良好.当代价比较小时，代价敏感贝叶斯网络分类器的总误分类代价相对于代价不敏感贝叶斯网络分类器的优势并不明显，但在高代价错误数方面的优势仍然明显。(C)2014爱思唯尔有限公司版权所有。
Cost-sensitive learning has received increased attention in recent years. However, in existing studies, most of the works are devoted to make decision trees cost-sensitive and very few works discuss cost-sensitive Bayesian network classifiers. In this paper, an instance weighting method is incorporated into various Bayesian network classifiers. The probability estimation of Bayesian network classifiers is modified by the instance weighting method, which makes Bayesian network classifiers cost-sensitive. The experimental results on 36 UCI data sets show that when cost ratio is large, the cost-sensitive Bayesian network classifiers perform well in terms of the total misclassification costs and the number of high cost errors. When cost ratio is small, the advantage of cost-sensitive Bayesian network classifiers is not so obvious in terms of the total misclassification costs, but still obvious in terms of the number of high cost errors, compared to the original cost-insensitive Bayesian network classifiers. (C) 2014 Elsevier B.V. All rights reserved.