Reducing Training Sets by NCN-based Exploratory Procedures

Lozano, M. ; Sánchez, JS. ; Pla, F.

Resumen: In this paper, a new approach to training set size reduction is presented. This scheme basically consists of defining a small number of prototypes that represent all the original instances. Although the ultimate aim of the algorithm proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over nine real datasets by comparing not only the reduction rate but also the classification accuracy with those of other condensing techniques.

Idioma: Inglés

Año: 2003

Publicado en: Lecture Notes in Computer Science 2652 (2003), 453-461
ISSN: 0302-9743

Tipo y forma: Article (PostPrint)