Efficient Coverage of Case Space with Active Learning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2009

Autores

Nuno Filipe Escudeiro (Autor) Outra; Alipio Mario Jorge (Autor) FCUP

Ata de Conferência Internacional

Título: Progress in Artificial Intelligence: Proceedings of the 14th Portuguese Conference on Artificial Intelligence, EPIA2009

Páginas: 411-422

14th EPIA - Portuguese Conference on Artificial Intelligence - EPIA 2009

Universidade de Aveiro, Portugal, 12 a 15 de Outubro de 2009

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 3 Citações

Classificação Científica

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

Outras Informações

ID Authenticus: P-003-RW2

DOI: 10.1007/978-3-642-04686-5_34

Abstract (EN): Collecting and annotating exemplary cases is a costly and critical task that is required in early stages of any classification process. Reducing labeling cost without degrading accuracy calls for a compromise solution which may be achieved with active learning. Common active learning approaches focus on accuracy and assume the availability of a pre-labeled set of exemplary cases covering all classes to learn. This assumption does not necessarily hold. In this paper we study the capabilities of a new active learning approach, d-Confidence, in rapidly covering the case space when compared to the traditional active learning confidence criterion, when the representativeness assumption is not met.. Experimental results also show that; d-Confidence reduces the number of queries required to achieve complete class coverage and tends to improve or maintain classification error.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12