A NEW HEURISTIC FUNCTION IN ANT-MINER APPROACH

Urszula Boryczka, Jan Kozak

2009

Abstract

In this paper, a novel rule discovery system that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants, where they search optimal solutions by considering both local heuristic and previous knowledge, observed by pheromone changes. In our approach we want to ensure the good performance of Ant-Miner by applying the new versions of heuristic functions in a main rule. We want to emphasize the role of the heuristic function by analyzing the influence of different propositions of these functions to the performance of Ant-Miner. The comparative study will be done using the 5 data sets from the UCI Machine Learning repository.

References

  1. Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees. Belmont C.A., Wadsworth.
  2. Chan, A. and Freitas, A. A. (2006). A new ant colony algorithm for multi-label alssification with applications in bioinformatics. In Proceedings of Genetic and Evolutionary Computation Conf. (GECCO' 2006), pages 27-34, San Francisco.
  3. Clark, P. and Boswell, R. (1991). Rule induction with CN2: some recent improvements. In Proc. European Working Session on Learning (EWSL-91), pages 151-163, Berlin. Springer Verlag, LNAI 482.
  4. Clark, P. and Niblett, T. (1989). The CN2 rule induction algorithm. Machine Learning, 3(4):261-283.
  5. Corne, D., Dorigo, M., and Glover, F. (1999). New Ideas in Optimization. Mc Graw-Hill, Cambridge.
  6. Dorigo, M. and Stützle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge.
  7. Freitas, A. A. and Johnson, C. G. (2003). Research cluster in swarm intelligence. Technical Report EPSRC Research Proposal GR/S63274/01 - Case for Support, Computing Laboratory, Laboratory of Kent, Kent.
  8. Galea, M. (2002). Applying swarm intelligence to rule induction. Master's thesis, MS thesis, University of Edingbourgh.
  9. Galea, M. and Shen, Q. (2006). Simultaneous ant colony optimization algorithms for learning linguistic fuzzy rules. In Agraham, A., Grosan, C., and Ramos, V., editors, Swarm Intelligence in Data Mining. Springer, Berlin.
  10. Kohavi, R. and Sahami, M. (1996). Error-based and entropy-based discretization of continuous features. In Proc. 2nd Intern. Conference Knowledge Discovery and Data Mining, pages 114-119.
  11. Liu, B., Abbas, H. A., and Kay, B. M. (2004). Classification rule discovery with ant colony optimization. IEEE Computational Intelligence Bulletin, 1(3):31-35.
  12. Martens, D., Backer, M. D., Haesen, R., Baesens, B., and Holvoet, T. (2006). Ants constructing rule-based classifiers. In Agraham, A., Grosan, C., and Ramos, V., editors, Swarm Intelligence in Data Mining. Springer, Berlin.
  13. Oakes, M. P. (2004). Ant colony optimization for stylometry: the federalist papers. In Proceedings of Recent Advances in Soft Computing (RASC - 2004), pages 86-91.
  14. Parpinelli, R. S., Lopes, H. S., and Freitas, A. A. (2002). An ant colony algorithm for classification rule discovery. In Abbas, H., Sarker, R., and Newton, C., editors, Data Mining: a Heuristic Approach. Idea Group Publishing, London.
  15. Parpinelli, R. S., Lopes, H. S., and Freitas, A. A. (2004). Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation, Special issue on Ant Colony Algorithms, 6(4):321-332.
  16. Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco.
Download


Paper Citation


in Harvard Style

Boryczka U. and Kozak J. (2009). A NEW HEURISTIC FUNCTION IN ANT-MINER APPROACH . In Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-85-2, pages 33-38. DOI: 10.5220/0001857700330038


in Bibtex Style

@conference{iceis09,
author={Urszula Boryczka and Jan Kozak},
title={A NEW HEURISTIC FUNCTION IN ANT-MINER APPROACH},
booktitle={Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2009},
pages={33-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001857700330038},
isbn={978-989-8111-85-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - A NEW HEURISTIC FUNCTION IN ANT-MINER APPROACH
SN - 978-989-8111-85-2
AU - Boryczka U.
AU - Kozak J.
PY - 2009
SP - 33
EP - 38
DO - 10.5220/0001857700330038