Tracking Recurring Concepts with Meta-learners

• Type: Article in International Scientific Journal
• Date: 2009

Authors

João Gama (Author) FEP; Petr Kosina (Author) Other

Journal

Title: Lecture Notes in Computer Science

Vol. 5816 No. 12

Pages: 423-434

ISSN: 0302-9743

Publisher: Springer Nature

Indexing

ISI Proceedings

ISI Web of Knowledge - 30 Citations

Scopus - 39 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science

Other information

Authenticus ID: P-003-RW3

DOI: 10.1007/978-3-642-04686-5_35

Abstract (EN): This work address data stream mining front dynamic environments where the distribution underlying the observations may change over time. In these contexts, learning algorithms must be equipped with change detection mechanisms. Several methods have been proposed able to detect and react to concept drift;. When a drift is signaled, most of the approaches use a forgetting mechanism, by releasing the current; model, and start, learning a, new decision model, Nevertheless, it; is not rare for the, concepts front history to reappear, for example seasonal changes. In this work we present; method that memorizes learnt; decision models whenever a concept drift is signaled. The system uses meta-learning techniques that characterize the domain of applicability of previous learnt models. The meta-learner can detect, re-occurrence of contexts and take pro-active actions by activating previous learnt models. The main benefit of this approach is that the proposed meta-learner is capable of selecting similar historical concepts, if there is one, without the knowledge of true classes of examples.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12