Tracking Recurring Concepts with Meta-learners

• Tipo: Artigo em Revista Científica Internacional
• Data: 2009

Autores

João Gama (Autor) FEP; Petr Kosina (Autor) Outra

Revista

Título: Lecture Notes in Computer Science

Vol. 5816 Nº 12

Páginas: 423-434

ISSN: 0302-9743

Editora: Springer Nature

Indexação

ISI Proceedings

ISI Web of Knowledge - 30 Citações

Scopus - 39 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências Físicas > Ciência de computadores

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12