A new fast prototype selection method based on clustering

J. Arturo Olvera-López

doi:10.1007/s10044-008-0142-x

A new fast prototype selection method based on clustering

2009-01-13

91

PRE

AI

J

J. Arturo Olvera-López *

J

Jesús Ariel Carrasco-Ochoa

J

José Fco. Martínez-Trinidad

DOI：10.1007/s10044-008-0142-x

原文链接

原文求助

摘要

En 中文

In supervised classification, a training set T is given to a classifier for classifying new prototypes. In practice, not all information in T is useful for classifiers, therefore, it is convenient to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time for classification or training could be reduced. In this work, we propose a new fast prototype selection method for large datasets, based on clustering, which selects border prototypes and some interior prototypes. Experimental results showing the performance of our method and comparing accuracy and runtimes against other prototype selection methods are reported.

Keyword:

Prototype selection

Supervised classification

Instance-based classifiers

Border prototypes

Data reduction

Clustering

A new fast prototype selection method based on clustering

摘要

期刊

机构