A New Heterogeneous Dissimilarity Measure for Data Classification

被引：3
|
作者：
Pereira, Cesar Lima [1 ]
Cavalcanti, George D. C. [1 ]
Ren, Tsang Ing [1 ]
机构：
[1] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil
来源：
22ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2010), PROCEEDINGS, VOL 2 | 2010年
关键词：
nearest neighbor; distance measure;
D O I：
10.1109/ICTAI.2010.132
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Instance-based learning algorithms typically suffer influences of dissimilarity functions. The problem is frequently related to the Nearest Neighbor rules of these algorithms. This paper will introduce a new dissimilarity measure, called Heterogeneous Centered Difference Measure, which is tested over many known databases. The results are compared with other distance functions.
引用
收藏
页码：373 / 374
页数：2
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