Classification rule discovery with ant colony optimization and improved quick reduct algorithm

被引：0

作者：

Jaganathan, P. ^{[1
]}

Thangavel, K. ^{[2
]}

Pethalakshmi, A. ^{[3
]}

Karnan, A. ^{[4
]}

机构：

[1] PSNA Coll Engn & Technol, Dept Comp Applicat, Dindigul 624622, Tamil Nadu, India

[2] Periyar Univ, Dept Comp Sci, Salem 636011, Tamil Nadu, India

[3] MVM Govt Arts Coll W, Dept Comp Sci, Dindigul 624001, Tamil Nadu, India

[4] Gandhigram Rural Inst Deemed Univ, Dept Comp Sci & Applicat, Coimbatore, Tamil Nadu, India

来源：

IMECS 2006: INTERNATIONAL MULTICONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS | 2006年

关键词：

ant colony optimization(ACO); quick reduct; improved quick reduct algorithm; classification;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. More recently, Parpinelli et al have applied ACO to data mining classification problems, where they introduced a classification algorithm called Ant Miner. In this paper, we present a system that combines both the proposed Improved Quickreduct algorithm for data preprocessing and ant miner. The proposed system was tested on standard data set and its performance is better than the original Ant Miner algorithm.

引用

页码：286 / +

页数：2

共 19 条

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