FWIB:模糊加权迭代贝叶斯算法

Tianhan Wang, Weidong Zhang, Dayong Lin
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引用次数: 0

摘要

在朴素贝叶斯(NB)的理论框架内,迭代贝叶斯(IB)作为一种重要的变体,提出了基于输出调整技术来缓解属性相互依赖问题,并有一定的性能提升。然而,与NB一样,IB在类不平衡数据上也存在分类偏差和抗噪声鲁棒性差的问题。为了解决这些问题,引入了代价敏感学习,并通过为训练数据分配模糊隶属度值,我们提出了一类模糊加权迭代贝叶斯算法(FWIB-CE, FWIB-HE, FWIB-CL-HL)来整合来自样本分布的更多先验信息。在10个典型的不平衡数据集上的实验结果表明,我们的方法在g均值评估下的分类性能明显优于NB和IB,并且对噪声干扰具有更高的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FWIB: Fuzzy Weighted Iterative Bayes Algorithm
Within the theoretical framework of Naive Bayes(NB), Iterative Bayes(IB), as an important variant, was proposed to alleviate the attribute interdependence problem based on output adjustment technique and shows certain performance enhancement. However, like NB, IB also suffers from biased classification and poor robustness against noise on class imbalanced data. To address these problems, cost-sensitive learning is introduced, and by assigning fuzzy-membership values to training data, we propose a class of Fuzzy Weighted Iterative Bayes algorithms (FWIB-CE, FWIB-HE, FWIB-CL-HL) to integrate more prior information from the sample distribution. The experimental results on ten typical imbalanced datasets show our methods have significant outperformance over NB and IB under G-mean evaluation in classification and have higher robustness against noise interference.
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