A concept-drifting detection algorithm for categorical evolving data
文献类型：会议论文
| 作者 | Cao, Fuyuan; Huang, Joshua Zhexue |
| 出版日期 | 2013 |
| 会议名称 | 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 |
| 会议地点 | Gold Coast, QLD, Australia |
| 英文摘要 | In data streams analysis, detecting concept-drifting is a very important problem for real-time decision making. In this paper, we propose a new method for detecting concept drifts by measuring the difference of distributions between two concepts. The difference is defined by approximation accuracy of rough set theory, which can also be used to measure the change speed of concepts. We propose a concept-drifting detection algorithm and analyze its complexity. The experimental results on a real data set with a half million records have shown that the proposed algorithm is not only effective in discovering the changes of concepts but also efficient in processing large data sets. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4979]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | 2013 |
| 推荐引用方式 GB/T 7714 | Cao, Fuyuan,Huang, Joshua Zhexue. A concept-drifting detection algorithm for categorical evolving data[C]. 见:17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013. Gold Coast, QLD, Australia. |
入库方式： OAI收割
来源：深圳先进技术研究院
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