Search-based class discretization

• Tipo: Artigo em Revista Científica Internacional
• Data: 1997

Autores

Torgo, L (Autor) FEP; Gama, J (Autor) FEP

Revista

Título: Lecture Notes in Computer Science

Vol. 387

Páginas: 266-273

ISSN: 0302-9743

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 17 Citações

Scopus - 23 Citações

Classificação Científica

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

Outras Informações

ID Authenticus: P-001-CR7

DOI: 10.1007/3-540-62858-4_91

Abstract (EN): We present a methodology that enables the use of classification algorithms on regression tasks. We implement this method in system RECLA that transforms a regression problem into a classification one and then uses an existent classification system to solve this new problem. The transformation consists of mapping a continuous variable into an ordinal variable by grouping its values into an appropriate set of intervals. We use misclassification costs as a means to reflect the implicit ordering among the ordinal values of the new variable. We describe a set of alternative discretization methods and, based on our experimental results, justify the need for a search-based approach to choose the best method. Our experimental results confirm the validity of our search-based approach to class discretization, and reveal the accuracy benefits of adding misclassification costs.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8