Affiliations: Department of Computer Science, National Chiao Tung University, 1001, Ta Hsueh Rd., Hsinchu 300, Taiwan, Republic of China, e-mail: tsaicj@cis.nctu.edu.tw | Department of Information and Learning Technology, National University of Tainan, 33, Sec. 2, Shu-Lin St. Tainan 700, Taiwan, Republic of China, e-mail: leeci@mail.nutn.edu.tw | Department of Information Management, National Dong Hwa University, No.1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 97401, Taiwan, Republic of China, e-mail: wpyang@mail.ndhu.edu.tw
Abstract: Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data block is basically a random sample from a stationary distribution, but many databases available violate this assumption. That is, the class of an instance may change over time, known as concept drift. In this paper, we propose a Sensitive Concept Drift Probing Decision Tree algorithm (SCRIPT), which is based on the statistical X2 test, to handle the concept drift problem on data streams. Compared with the proposed methods, the advantages of SCRIPT include: a) it can avoid unnecessary system cost for stable data streams; b) it can immediately and efficiently corrects original classifier while data streams are instable; c) it is more suitable to the applications in which a sensitive detection of concept drift is required.
Keywords: data mining, data streams, incremental learning, decision tree, concept drift
Journal: Informatica, vol. 19, no. 1, pp. 135-156, 2008
