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

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.

[1]  Alok Singh,et al.  An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem , 2009, Appl. Soft Comput..

[2]  Tiago Ferra de Sousa,et al.  Particle Swarm based Data Mining Algorithms for classification tasks , 2004, Parallel Comput..

[3]  Mahdi Abadi,et al.  An ABC-AIS Hybrid Approach to Dynamic Anomaly Detection in AODV-Based MANETs , 2011, 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications.

[4]  Emel Kizilkaya Aydogan,et al.  Mining classification rules with Reduced MEPAR-miner Algorithm , 2008, Appl. Math. Comput..

[5]  Ian H. Witten,et al.  Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.

[6]  Mehmet Fatih Tasgetiren,et al.  A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem , 2011, Inf. Sci..

[7]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[8]  Manuel Ivan Rodriguez-Borbon,et al.  Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm , 2013, Expert Syst. Appl..

[9]  Stephen F. Smith,et al.  Flexible Learning of Problem Solving Heuristics Through Adaptive Search , 1983, IJCAI.

[10]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[11]  Filippo Neri,et al.  Search-Intensive Concept Induction , 1995, Evolutionary Computation.

[12]  Alex Alves Freitas,et al.  cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes , 2008, ANTS Conference.

[13]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[14]  Alex Alves Freitas,et al.  A hybrid PSO/ACO algorithm for classification , 2007, GECCO '07.

[15]  Alex Alves Freitas,et al.  Data mining with an ant colony optimization algorithm , 2002, IEEE Trans. Evol. Comput..

[16]  Liang Zhao,et al.  Classification rule discovery with DE/QDE algorithm , 2010, Expert Syst. Appl..

[17]  Kenneth A. De Jong,et al.  Using genetic algorithms for concept learning , 1993, Machine Learning.

[18]  William W. Cohen Fast Effective Rule Induction , 1995, ICML.

[19]  Rosni Abdullah,et al.  Protein Conformational Search Using Bees Algorithm , 2008, 2008 Second Asia International Conference on Modelling & Simulation (AMS).

[20]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[21]  John H. Holland,et al.  Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .