A rule merging technique for handling noise in inductive learning

被引：8

作者：

Pham, DT ^{[1
]}

Bigot, S ^{[1
]}

Dimov, SS ^{[1
]}

机构：

[1] Cardiff Univ, Intelligent Syst Lab, Mfg Engn Ctr, Sch Engn, Cardiff CF24 OYF, S Glam, Wales

来源：

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE | 2004年 / 218卷 / 10期

关键词：

machine learning; rule induction; pruning; noise handling;

D O I：

10.1243/0954406042369017

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Inductive learning algorithms are used for extracting IF-THEN rules from examples. The main weakness of most existing algorithms is their poor ability to handle data containing noise. This problem is even more severe when inductive learning techniques are applied to real engineering data. The paper presents a new pruning technique that improves significantly the performance of the RULES family of inductive learning algorithms. The technique is designed for RULES-5, the latest algorithm in the family, but could readily be applied to rule sets created by other algorithms.

引用

页码：1255 / 1268

页数：14