A new rule ranking model for Associative Classification using a hybrid Artificial Intelligence technique

Author

Najeeb, Moath M. ; Sheikh, A.E. ; Nababteh, Mohammed

Author_Institution

Fac. of Inf. Syst. & Technol., Arab Acad. for Banking & Financial Sci., Amman, Jordan

fYear

2011

fDate

27-29 May 2011

Firstpage

231

Lastpage

235

Abstract

Rule ranking is a crucial step in Associative Classification (AC), AC algorithms proposed many ranking methods which aim to improve the accuracy of the classifier. In this paper we propose a new model in rule ranking, namely Hybrid-RuleRank, which employs a hybrid Artificial Intelligence (AI) technique that combines Simulated Annealing (SA) with Genetic Algorithm (GA), the new model tested against 11 data sets from UCI Machine Learning Repository, and the experimental results show that our model enhances the accuracy of the classifier.

Keywords

data mining; genetic algorithms; learning (artificial intelligence); pattern classification; simulated annealing; AI; GA; Hybrid-RuleRank; SA; UCI machine learning repository; associative classification; data mining; genetic algorithm; hybrid artificial intelligence technique; rule ranking model; simulated annealing; Accuracy; Annealing; Biological system modeling; Breast; Iris; Machine learning; Prediction methods; Artificial Intelligence; Associative Classification; Genetic Algorithm; Simulated Annealing;

fLanguage

English

Publisher

ieee

Conference_Titel

Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on

Conference_Location

Xi´an

Print_ISBN

978-1-61284-485-5

Type

conf

DOI

10.1109/ICCSN.2011.6013816

Filename

6013816