EXPLOITING UNLABELED DATA IN CONCEPT DRIFT LEARNING

Authors

  • Dwi Hendratmo Widyantoro School of Electrical Engineering and Informatics, Bandung Institute of Technology

DOI:

https://doi.org/10.9744/informatika.8.1.pp.%2054-62

Keywords:

concept drift learning, unlabeled data, persistence assumption.

Abstract

Learning unlabeled data in a drifting environment still receives little attention. This paper presents a concept tracker algorithm for learning concept drift that exploits unlabeled data. In the absence of complete labeled data, instance classes are identified using a concept hierarchy that is incrementally constructed from data stream (mostly unlabeled data) in unsupervised mode. The persistence assumption in temporal reasoning is then applied to infer target concepts. Empirical evaluation that has been conducted on information-filtering domains demonstrates the effectiveness of this approach.

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Published

2007-07-04

Issue

Section

Articles