Abstract
This paper presents a new ant-based algorithm for the multi-objective classification problem. The new algorithm called MulO-AntMiner (Multi-Objective Ant-Miner) is an improved version of the Ant-Miner algorithm, the first implementation of the ant colony algorithm for discovering classification rules. The fundamental principles in the proposed algorithm are almost similar to those in original Ant-Miner; even though, in our work there are two or more class attributes to be predicted. As a result, the consequent of a classification rule contains multiple predictions, each prediction involving a different class attribute. We have compared the performance of MulO-AntMiner with two other algorithms namely the C4.5 algorithm and the original Ant-Miner.
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
Parepinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computing 6, 321–332 (2002)
Quinlan, J.R.: C4.5: Programs for Machine learning. Morgan Kaufman, San Mateo (1993)
Blockeel, H., De Raedt, L., Ramon, J.: Top-down induction of clustering trees. In: 15th International Conference on Machine Learning, pp. 55–63 (1998)
Suzuki, E., Gotoh, M., Choki, Y.: Bloomy Decision Tree for Multi-objective Classification. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 436–447. Springer, Heidelberg (2001)
Zenko, B., Dzeroski, S.: Learning classification rules for multiple target Attributes. In: 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 454–465 (2008)
Li, X.B., Sweigart, J.R., Teng, J.T.C., Donohue, J.M., Thombs, L.A., Wang, S.M.: Multivariate decision trees using linear discriminants and tabu search. IEEE Transactions on Systems Man and Cybernetics Part A–Systems and Humans 33, 194–205 (2003)
Hanafi, S., Yanev, N.: Tabu search approaches for solving the two-group classification problem. Annals of Operations Research (2009)
Mohamadi, H., Habibi, J., Abadeh, M.S., Saadi, H.: Data mining with a simulated annealing based fuzzy classification system. Pattern Recognition 41, 1841–1850 (2008)
Dong, G., Lai, K.K., Zhou, L.: Simulated Annealing Based Rule Extraction Algorithm for Credit Scoring Problem. Computational Sciences and Optimization 2, 22–26 (2009)
De Jong, K.A., Spears, W.M., Gordon, D.F.: Using genetic algorithms for concept learning. Genetic algorithms for machine learning. In: John, J., Grefenstette (eds.) A Special Issue of Machine Learning, vol. 13, pp. 161–188 (1993)
Carvalho, D.R., Avila, B.C., Freitas, A.A.: A Hybrid Genetic Algorithm/Decision Tree Approach for Coping with Unbalanced Cases. In: Third International Conference Practical Applications of Knowledge Discovery and Data Mining, pp. 61–70 (1999)
Chen, J., Wang, X., Zhai, J.: Pruning Decision Tree Using Genetic Algorithms. In: International Conference on Artificial Intelligence and Computational Intelligence, pp. 244–248 (2009)
Sousa, T., Silva, A., Neves, A.: Particle Swarm based Data Mining Algorithms for classification tasks. Parallel Computing 30, 767–783 (2004)
Gandhi, K.R., Karnan, M., Kannan, S.: Classification Rule Construction Using Particle Swarm Optimization Algorithm for Breast Cancer Data Sets. In: The International Conference on Signal Acquisition and Processing, pp. 233–237 (2010)
Nejad, N.Z., Bakhtiary, A.H., Analoui, M.: Classification Using Unstructured Rules and Ant Colony Optimization. In: IMECS Conference, pp. 506–510 (2008)
Freitas, A.A., Otero, F.E.B., Johnson, C.G.: c-Ant-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 48–59. Springer, Heidelberg (2008)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Interscience, Hoboken (1991)
An, A.: Learning classification rules from data. Computer and Mathematics with Applications 45, 737–748 (2003)
Bramer, M.A.: Using J-pruning to reduce overfitting in classification. Knowledge Based Systems 15, 301–308 (2002)
Smyth, P., Goodman, R.M.: An Information Theoretic Approach to Rule Induction from Databases. IEEE Transactions on Knowledge and Data Engineering 4, 301–316 (1991)
Weiss, S.M., Kulikowski, C.A.: Computer Systems that Learn. Morgan Kaufmann Publishers, San Francisco (1991)
Hettich, D.S., Blake, C., Merz, C. J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html 51, 52, 128
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Said, N., Hammami, M., Ghedira, K. (2011). MulO-AntMiner: A New Ant Colony Algorithm for the Multi-objective Classification Problem. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21887-3_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-21887-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21886-6
Online ISBN: 978-3-642-21887-3
eBook Packages: Computer ScienceComputer Science (R0)