Exploiting Unlabeled Data in Concept Drift Learning  

Oleh:

Widyantoro, Dwi Hendratmo

Jenis: Article from Journal - ilmiah nasional - tidak terakreditasi DIKTI
Dalam koleksi: Jurnal Informatika vol. 8 no. 1 (May 2007), page 54-62.
Topik: LEARNING; concept drift learning; unlabeled data; persistence assumption
Fulltext: dwi hendratmo widyantoro.pdf (179.36KB)

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Perpustakaan Pusat (Semanggi) Nomor Panggil: JJ103 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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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