Recurrent concepts in data streams classification

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

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

Revista

Título: Knowledge and Information Systems: an International Journal

Editora: Springer

Indexação

ISI Web of Knowledge - 31 Citações

ISI Web of Science

Scopus - 41 Citações

Classificação Científica

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

Outras Informações

ID Authenticus: P-009-RSE

DOI: 10.1007/s10115-013-0654-6

Abstract (EN): This work addresses the problem of mining data streams generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnose degradations of this process, using change detection mechanisms, and self-repair the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learner can detect recurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models. The experimental evaluation on three text mining problems demonstrates the main advantages of the proposed system: it provides information about the recurrence of concepts and rapidly adapts decision models when drift occurs.

Idioma: Inglês

Tipo (Avaliação Docente): Científica