Sampled Bayesian Network Classifiers for Class-Imbalance and Cost-Sensitive Learning

Liangxiao Jiang, Chaoqun Li, Z. Cai, Harry Zhang
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引用次数: 15

Abstract

In many real-world applications, it is often the case that the class distribution of instances is imbalanced and the costs of misclassification are different. Thus, class-imbalance and cost-sensitive learning have attracted much attention from researchers. Sampling is one of the widely used approaches in dealing with the class imbalance problem, which alters the class distribution of instances so that the minority class is well represented in the training data. In this paper, we study the effect of sampling the natural training data on state-of-the-art Bayesian network classifiers, such as Naive Bayes (NB), Tree Augmented Naïve Bayes (TAN), Averaged One-Dependence Estimators (AODE), Weighted Average of One-Dependence Estimators (WAODE), and Hidden naive Bayes (HNB) and propose sampled Bayesian network classifiers. Our experimental results on a large number of UCI datasets show that our sampled Bayesian network classifiers perform much better than the ones trained from the natural training data especially when the natural training data is highly imbalanced and the cost ratio is high enough.
类不平衡和代价敏感学习的抽样贝叶斯网络分类器
在许多实际应用程序中,实例的类分布往往是不平衡的,错误分类的代价是不同的。因此,班级失衡和成本敏感学习受到了研究者的广泛关注。采样是处理类不平衡问题的一种广泛使用的方法,它改变了实例的类分布,使少数类在训练数据中得到很好的代表。本文研究了自然训练数据采样对朴素贝叶斯(NB)、树增广Naïve贝叶斯(TAN)、平均一相关估计器(AODE)、一相关估计器加权平均(WAODE)和隐朴素贝叶斯(HNB)等最先进的贝叶斯网络分类器的影响,并提出了采样贝叶斯网络分类器。我们在大量UCI数据集上的实验结果表明,我们的抽样贝叶斯网络分类器比自然训练数据训练的分类器性能要好得多,特别是在自然训练数据高度不平衡和成本比足够高的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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