A New Pruning Method for Associative Classification using Simulated Annealing Technique

被引：0

作者：

Najeeb, Moath M. ^{[1
]}

Alzoubi, Bilal R. ^{[1
]}

Al-Zghoul, Musab B. ^{[1
]}

Al-Qahtani, Abdullah S. ^{[1
]}

机构：

[1] Umm Al Qura Univ, Mecca, Saudi Arabia

来源：

INNOVATION VISION 2020: SUSTAINABLE GROWTH, ENTREPRENEURSHIP, AND ECONOMIC DEVELOPMENT, VOLS 1-4 | 2012年

关键词：

Associative Classification; Pruning Methods; Simulated Annealing;

D O I：

暂无

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Associative Classification (AC) algorithms normally produce large number of rules during "Rule Generation" step, so many of these algorithms use various pruning methods to eliminate the redundant or misleading rules, and consequently the size of the classifier will be reduced and the classification accuracy will be enhanced. In this paper we propose a new pruning method, namely PSA (Pruning based on Simulated Annealing), the new method tested against 7 data sets from UCI Machine Learning Repository, and the experimental results show that PSA method enhances the accuracy of the classifier.

引用

页码：2021 / 2029

页数：9

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