BeeMiner: A novel artificial bee colony algorithm for classification rule discovery

Author

Talebi, M. ; Abadi, Mahdi

Author_Institution

Fac. of Eng., Tarbiat Modares Univ., Tehran, Iran

fYear

2014

fDate

4-6 Feb. 2014

Firstpage

1

Lastpage

5

Abstract

Artificial bee colony (ABC) is a new population-based algorithm that has shown promising results in the field of optimization. In this paper, we propose BeeMiner, a novel ABC algorithm for discovering classification rules. BeeMiner differs from the original ABC because it uses an information-theoretic heuristic function (IHF) to guide the bees to search across the most promising areas of the search space. We compare the performance of BeeMiner with those of J48, JRip, and PART on nine benchmark datasets from the UCI Machine Learning Repository. The results show that BeeMiner is competitive with J48, JRip, and PART in terms of the predictive accuracy.

Keywords

data mining; learning (artificial intelligence); optimisation; pattern classification; search problems; ABC algorithm; BeeMiner; IHF; UCI machine learning repository; benchmark datasets; classification rule discovery; data mining; discovering classification rules; information theoretic heuristic function; novel artificial bee colony algorithm; search space; Accuracy; Breast tissue; Classification algorithms; Data mining; Glass; Optimization; Training; artificial bee colony; classification rule discovery; data mining; information theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems (ICIS), 2014 Iranian Conference on

Conference_Location

Bam

Print_ISBN

978-1-4799-3350-1

Type

conf

DOI

10.1109/IranianCIS.2014.6802576

Filename

6802576