Abstract
Ant programming (AP) is a kind of automatic programming that generates computer programs by using the ant colony optimization metaheuristic. It has recently demonstrated a good generalization ability when extracting classification rules. We extend the investigation on the application of AP to classification, developing an algorithm that addresses rules’ evaluation using a novel multi-objective approach specially devised for the classification task. The algorithm proposed also incorporates an evolutionary computing niching procedure to increment the diversity of the population of programs found so far. Results obtained by this algorithm are compared with other three genetic programming algorithms and other industry standard algorithms from different areas, proving that multi-objective AP is a good technique at tackling classification problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bojarczuk, C.C., Lopes, H.S., Freitas, A.A., Michalkiewicz, E.L.: A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets. Artificial Intelligence in Medicine 30, 27–48 (2004)
Tan, K.C., Tay, A., Lee, T.H., Heng, C.M.: Mining multiple comprehensible classification rules using genetic programming. In: CEC 2002, pp. 1302–1307 (2002)
Abbass, H.A., Hoai, X., Mckay, R.I.: AntTAG: A new method to compose computer programs using colonies of ants. In: IEEE CEC 2002, pp. 1654–1659 (2002)
Dorigo, M., Stützle, T.: The Ant Colony Optimization metaheuristic: Algorithms, Applications and Advances. Kluwer Academic Publishers (2002)
Olmo, J.L., Romero, J.R., Ventura, S.: Using ant programming guided by grammar for building rule-based classifiers. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 41(6), 1585–1599 (2011)
Espejo, P., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40(2), 121–144 (2010)
Angus, D., Woodward, C.: Multiple objective ant colony optimisation. Swarm Intelligence 3(1), 69–85 (2009)
Dehuri, S., Patnaik, S., Ghosh, A., Mall, R.: Application of elitist multi-objective genetic algorithm for classification rule generation. Appl. Soft Comput. 8, 477–487 (2008)
Olmo, J.L., Romero, J.R., Ventura, S.: A grammar based ant programming algorithm for mining classification rules. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 225–232 (2010)
Frank, A., Asuncion, A.: UCI machine learning repository (2010)
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI 1993, pp. 1022–1029 (1993)
Parpinelli, R., Freitas, A.A., Lopes, H.S.: Data mining with an ant colony optimization algorithm. IEEE Trans. on Evol. Computation 6, 321–332 (2002)
Martens, D., De Backer, M., Vanthienen, J., Snoeck, M., Baesens, B.: Classification with ant colony optimization. IEEE Transactions on Evolutionary Computation 11, 651–665 (2007)
Holden, N., Freitas, A.A.: A hybrid PSO/ACO algorithm for discovering classification rules in data mining. J. Artif. Evol. App, 2:1–2:11 (2008)
Cano, A., Zafra, A., Ventura, S.: An EP algorithm for learning highly interpretable classifiers. In: ISDA 2011, pp. 325–330 (2011)
Cohen, W.: Fast Effective Rule Induction. In: ICML 1995, pp. 115–123 (1995)
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: ICML 1998, pp. 144–151 (1998)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Olmo, J.L., Romero, J.R., Ventura, S. (2012). Multi-Objective Ant Programming for Mining Classification Rules. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds) Genetic Programming. EuroGP 2012. Lecture Notes in Computer Science, vol 7244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29139-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-29139-5_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29138-8
Online ISBN: 978-3-642-29139-5
eBook Packages: Computer ScienceComputer Science (R0)