Causality-Based Attribute Weighting via Information Flow and Genetic Algorithm for Naive Bayes Classifier
Causality-Based Attribute Weighting via Information Flow and Genetic Algorithm for Naive Bayes Classifier
复制标题
通过信息流和遗传算法对朴素贝叶斯分类器进行基于因果关系的属性加权
DOI：
10.1109/access.2019.2947568
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Liu, Kefeng
中科院分区：
文献类型：
--
作者：
Li, Ming;Liu, Kefeng
朴素贝叶斯分类器（NBC）是数据挖掘和机器学习中一种有效的分类技术，它基于属性条件独立性假设。然而，这一假设在实际应用中很少成立，因此许多研究已经通过属性加权来减轻这一假设。据我们所知，几乎所有的研究都是根据相关性度量或分类精度来计算属性权重的。在本文中，我们提出了一种新的基于因果关系的属性加权方法，建立加权NBC称为IFG-WNBC，其中因果信息流（IF）理论和遗传算法（GA），以寻找最佳的权重。IF的引入，从因果关系而非相关性的角度，产生了一种全新的权重度量标准。遗传算法中的种群初始化也得到了改进，基于IF的权重有效的优化。在UCI数据集上进行的多组对比实验表明，IFG-WNBC算法在分类精度和运行时间上均优于经典NBC算法和其他常用的加权NBC算法。
Naive Bayes classifier (NBC) is an effective classification technique in data mining and machine learning, which is based on the attribute conditional independence assumption. However, this assumption rarely holds true in real-world applications, so numerous researches have been made to alleviate the assumption by attribute weighting. To the best of our knowledge, almost all studies have calculated attribute weights according to correlation measure or classification accuracy. In this paper, we propose a novel causality-based attribute weighting method to establish the weighted NBC called IFG-WNBC, where causal information flow (IF) theory and genetic algorithm (GA) are adopted to search for optimal weights. The introduction of IF produces a bran-new weight measure criterion from the angle of causality other than correlation. The population initialization in GA is also improved with IF-based weights for efficient optimization. Multi-set of comparison experiments on UCI data sets demonstrate that IFG-WNBC achieves superiority over classic NBC and other common weighted NBC algorithms in classification accuracy and running time.