EXTRACTING LAWS FROM DECISION TABLES - A ROUGH SET APPROACH

被引：84

作者：

SKOWRON, A

机构：

[1] Institute of Mathematics, Warsaw University, Warsaw, 02-097

来源：

COMPUTATIONAL INTELLIGENCE | 1995年 / 11卷 / 02期

关键词：

REASONING UNDER UNCERTAINTY; ROUGH SETS; KNOWLEDGE DISCOVERY; MACHINE LEARNING;

D O I：

10.1111/j.1467-8640.1995.tb00039.x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present some methods, based on the rough set and Boolean reasoning approaches, for extracting laws from decision tables. First we discuss several procedures for decision rules synthesis from decision tables. Next we show how to apply some near-to-functional relations between data to data filtration. Two methods of searching for new classifiers (features) are described: searching for new classifiers in a given set of logical formulas, and searching for some functions approximating near-to-functional relations.

引用

页码：371 / 388

页数：18