Dynamic discretization of continuous attributes

• Tipo: Artigo em Revista Científica Internacional
• Data: 1998

Autores

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

Revista

Título: Lecture Notes in Computer Science

Vol. 387

Páginas: 160-169

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-001-9PE

DOI: 10.1007/3-540-49795-1_14

Abstract (EN): Discretization of continuous attributes is an important task for certain types of machine learning algorithms. Bayesian approaches, for instance, require assumptions about data distributions. Decision Trees on the other hand, require sorting operations to deal with continuous attributes, which largely increase learning times. This paper presents a new method of discretization, whose main characteristic is that it takes into account interdependencies between attributes. Detecting interdependencies can be seen as discovering redundant attributes. This means that our method performs attribute selection as a side effect of the discretization. Empirical evaluation on five benchmark datasets from UCI repository, using C4.5 and a naive Bayes, shows a consistent reduction of the features without loss of generalization accuracy.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10