A New Discernibility Metric and Its Application on Pattern Classification and Feature Evaluation

被引：0
|
作者：
Voulgaris, Zacharias
机构：
来源：
ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, PT II | 2011年 / 364卷
关键词：
Discernibility; Feature Evaluation; Classification Performance; Dataset Evaluation; Information Content; Classification; Pattern Recognition; CLASS SEPARABILITY; SELECTION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel evaluation metric is introduced, based on the Discernibility concept. This metric, the Distance-based Index of Discernibility (DID) aims to provide an accurate and fast mapping of the classification performance of a feature or a dataset. DID has been successfully implemented in a program which has been applied to a number of datasets, a few artificial features and a typical benchmark dataset. The results appear to be quite promising, verifying the initial hypothesis.
引用
收藏
页码：27 / 35
页数：9
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