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摘要: 概念漂移探测是数据流挖掘具有挑战意义的研究难点，属性约简是粗糙集理论的研究核心.从概念漂移的角度研究了粗糙集理论的属性约简，从粗糙集属性约简的角度研究了概念漂移，将概念漂移和属性约简进行分析比较，指出了它们之间的区别和联系.提出了基于属性依赖度和条件熵的概念漂移探测准则，并将两种常用的概念漂移探测准则与属性依赖度、条件熵探测准则进行了比较.属性依赖度和条件熵兼具分类准确率的可实验检验和联合概率分布可进行理论分析的优点，还可以进行属性约简（或特征选择）.实验结果显示，属性依赖度、条件熵和分类准确率都能有效地探测概念漂移，但是，与分类准确率相比，属性依赖度和条件熵在探测概念漂移时可以增加可重用性，减少工作量.属性约简和概念漂移之间关系的研究为属性约简、概念漂移的研究提供了新方法，为粗糙集、粒计算进一步融入大数据时代潮流提供了新思路.Abstract: Concept drift detection is a challenge to data stream mining, and attribute reduction is a focus of rough set theory. In this paper the phenomenon of concept drift is analyzed from the viewpoint of attribute reduction, and on the other hand, attribute reduction is analyzed deeply from the viewpoint of concept drift. The difference and the relation between concept drift and attribute reduction are investigated. Two criteria of concept drift detection, which rely on dependence of conditional attributes and conditional entropy, are presented. Analysis and comparison are conducted between two classical criteria of concept drift detection and two criteria of attribute reduction. Dependence of conditional attributes and conditional entropy possess the advantages of both testability of classification accuracy and theoretic analyzability of joint probability. Experimental results show that classification accuracy, dependence of conditional attributes and conditional entropy are valid and efficient for detecting concept drift. Moreover, compared with classification accuracy, the results can be reused and the workload can be reduced when dependence of conditional attributes and conditional entropy are employed to detect concept drift. The study on the relation between concept drift and attribute reduction provides a new method for attribute reduction and concept drift detection, as well as a new idea for rough set theory and granular computing to blend in the era of big data.
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Key words:
- F-rough sets /
- data streams /
- concept drift /
- attribute reduction /
- conditional information entropy
1) 本文责任编委 祝峰 -
图 1 滑动窗口的大小为10 000, 且相邻窗口之间没有重复
Fig. 1 The size of sliding windows is 10 000 without repeat
图 2 滑动窗口的大小为5 000, 且相邻窗口之间没有重复
Fig. 2 The size of sliding windows is 5 000 without repeat
图 3 滑动窗口的大小为5 000, 且相邻窗口之间有10 %的重复
Fig. 3 The size of sliding windows is 5 000 with 10 % repeat
图 4 滑动窗口的大小为10 000, 且相临窗口之间有10 %的重复
Fig. 4 The size of sliding windows is 10 000 with 10 % repeat
表 1 决策子系统DT1
Table 1 A decision subsystem DT1
U1 a b c d x1 0 0 1 1 x2 1 1 0 1 x3 0 1 0 0 x4 1 1 0 1 
下载: 导出CSV
表 2 决策子系统DT2
Table 2 A decision subsystem DT2
U2 a b c d y1 0 1 0 0 y2 1 1 0 1 y3 1 1 0 1 y4 0 1 0 0 y5 1 2 0 0 y6 1 2 0 1
下载: 导出CSV
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