Unordered rule discovery using Ant Colony Optimization  

作　　者：KHAN Salabat BAIG Abdul Rauf ALI Armughan HAIDER Bilal KHAN Farman Ali DURRANI Mehr Yahya ISHTIAQ Muhammad 

机构地区：[1]Department of Computer Science,COMSATS Institute of Information Technology,Attock Campus,Pakistan [2]Department of Computer Science,National University of Computer and Emerging Sciences,Islamabad,Pakistan [3]Al Imam Mohammad Ibn Saud Islamic University (IMSIU),College of Computer and Information Sciences,Riyadh,Saudi Arabia

出　　处：《Science China(Information Sciences)》2014年第9期185-199,共15页中国科学（信息科学）（英文版）

摘　　要：In this article,a novel unordered classification rule list discovery algorithm is presented based on Ant Colony Optimization（ACO）. The proposed classifier is compared empirically with two other ACO-based classification techniques on 26 data sets,selected from miscellaneous domains,based on several performance measures. As opposed to its ancestors,our technique has the flexibility of generating a list of IF-THEN rules with unrestricted order. It makes the generated classification model more comprehensible and easily interpretable.The results indicate that the performance of the proposed method is statistically significantly better as compared with previous versions of AntMiner based on predictive accuracy and comprehensibility of the classification model.In this article,a novel unordered classification rule list discovery algorithm is presented based on Ant Colony Optimization（ACO）. The proposed classifier is compared empirically with two other ACO-based classification techniques on 26 data sets,selected from miscellaneous domains,based on several performance measures. As opposed to its ancestors,our technique has the flexibility of generating a list of IF-THEN rules with unrestricted order. It makes the generated classification model more comprehensible and easily interpretable.The results indicate that the performance of the proposed method is statistically significantly better as compared with previous versions of AntMiner based on predictive accuracy and comprehensibility of the classification model.

关 键 词：CLASSIFICATION ant colony optimization data mining unordered rule set COMPREHENSIBILITY pattern recognition 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]