Learning concept approximation from uncertain decision tables

被引：0

作者：

Hoa, NS ^{[1
]}

Son, NH ^{[1
]}

机构：

[1] Polish Japanese Inst Informat Technol, Warsaw, Poland

来源：

MONITORING, SECURITY, AND RESCUE TECHNIQUES IN MULTIAGENT SYSTEMS | 2005年

关键词：

D O I：

10.1007/3-540-32370-8_18

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 a hierarchical learning approach to approximation of complex concept from experimental data using inference diagram as a domain knowledge. The solution, based on rough set and rough mereology theory, require to design a learning method from uncertain decision tables. We examine the effectiveness of the proposed approach by comparing it with standard learning approaches with respect to different criteria on artificial data sets generated by a traffic road simulator.

引用

页码：249 / 260

页数：12

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