Comprehensive vs. comprehensible classifiers in logical analysis of data

被引：20
作者：
Alexe, Gabriela [1 ]
Alexe, Sorin [1 ]
Hammer, Peter L. [1 ]
Kogan, Alexander [1 ]
机构：
[1] Rutgers State Univ, RUTCOR, Piscataway, NJ 08854 USA
来源：
DISCRETE APPLIED MATHEMATICS | 2008年 / 156卷 / 06期
关键词：
Logical analysis of data (LAD); pattern; prime pattern; spanned pattern; pattern-based classifier;
D O I：
10.1016/j.dam.2005.02.035
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The main objective of this paper is to compare the classification accuracy provided by large, comprehensive collections of patterns (rules) derived from archives of past observations, with that provided by small, comprehensible collections of patterns. This comparison is carried out here on the basis of an empirical study, using several publicly available data sets. The results of this study show that the use of comprehensive collections allows a slight increase of classification accuracy, and that the "cost of comprehensibility" is small. (C) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码：870 / 882
页数：13
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