关键词: 最小距离分类器, 欧式距离, 多重限制分类器, 决策树分类器

Abstract: Minimum distance classifier is a simple and effective classification method. To improve its classification performance, the main methods were selecting the more effective distance measure. On the basis of analyzing the classification principle of decision tree classifier and parallelpiped classifier, a new classification method based on normalized Euclidian distance, called WMDC(weighted minimum distance classifier), was proposed. By adding weight define with nominal and string attributes and adding range restriction of attribute’s value, WMDC extended applicability of MDC(minimum distance classifier) using normalized Euclidian distance and improved its classification accuracy. Experiment results show this model has good classification accuracy in most data sets.

Key words: minimum distance classifier, Euclidian distance, parallelpiped classifier, decision tree classifier