Online Markov Blanket Discovery With Streaming Features

Dianlong You, Ruiqi Li, Miaomiao Sun, Xinju Ou, Shunpan Liang, Fuyong Yuan
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引用次数: 2

Abstract

The Markov blanket (MB) in Bayesian networks has attracted much attention since the MB of a target attribute (T) is the minimal feature subset with maximum prediction ability for classification. Nevertheless, traditional MB discovery methods such as IAMB, HITON-MB, and MMMB are not suitable for streaming features. Meanwhile, online feature selection with streaming features (OSFSF) methods such as Alpha-investing and SAOLA, focus only on the relevance and ignores causality of the features, they cannot mine the MB, or only find the parents and children (PC) such as OSFS. Therefore, these methods have weaker interpretability and do not have sufficient prediction accuracy. We propose a novel algorithm for online markov blanket discovery with streaming features to tackle abovementioned issues, named OMBSF. OMBSF finds the MB of T containing parents, children, and spouses, discards false positives from the PC and spouse sets online, distinguishes between the PC and spouse in real-time. An empirical study demonstrates that OMBSF finds a more accurate MB when the volume of features is high and it significantly improves the prediction accuracy than other algorithms. Moreover, OMBSF obtains a bigger feature subset than that obtained by OSFS, demonstrating that OMBSF can identify the spouse in the MB that are not identified using OSFS.
具有流特性的在线马尔可夫毯子发现
由于目标属性(T)的马尔可夫包层(MB)是分类预测能力最大的最小特征子集,因此贝叶斯网络中的马尔可夫包层(MB)备受关注。但是,传统的MB发现方法如IAMB、HITON-MB、MMMB等并不适合流媒体特性。同时,基于流特征(flow features, OSFSF)的在线特征选择方法,如Alpha-investing和SAOLA,只关注特征之间的相关性,忽略了特征之间的因果关系,不能挖掘MB,或者像OSFS那样只寻找父母和孩子(parent and children, PC)。因此,这些方法的可解释性较弱,预测精度不够。为了解决上述问题,我们提出了一种具有流特征的在线马尔可夫毯子发现新算法,称为OMBSF。OMBSF查找包含父母、子女和配偶的MB (T),丢弃在线PC和配偶集的假阳性,实时区分PC和配偶。实证研究表明,当特征量较大时,OMBSF找到了更准确的MB,显著提高了其他算法的预测精度。此外,OMBSF得到的特征子集比OSFS得到的更大,说明OMBSF可以识别出MB中OSFS无法识别的配偶。
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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