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Medland, Matthew; Otero, Fernando E.B.; Freitas, Alex A. (2012)
Publisher: Springer Berlin Heidelberg
Languages: English
Types: Unknown
Subjects: QA76
Ant Colony Optimisation (ACO) has been successfully applied to the classification task of data mining in the form of Ant-Miner. A new extension of Ant-Miner, called cAnt-MinerPB, uses the ACO procedure in a different fashion. The main difference is that the search in cAnt-MinerPB is optimised to find the best list of rules, whereas in Ant-Miner the search is optimised to find the best individual rule at each step of the sequential covering, producing a list of best rules. We aim to improve cAnt-MinerPB in two ways, firstly by dynamically finding the rule quality function which is used while the rules are being pruned, and secondly improving the rule-list quality function which is used to guide the search. We have found that changing the rule quality function has little effect on the overall performance, but that by improving the rule-list quality function we can positively affect the discovered lists of rules.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • 2. Dorigo, M., Stu¨zle, T.: Ant Colony Optimization. The MIT Press (2004)
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    • 6. Garc´ıa, S., Herrera, F.: An Extension on 'Statistical Comparisons of Classifiers over Multiple Data Sets' for all Pairwise Comparisons. JMLR 9, 2677-2694 (2008)
    • 7. Janssen, F., Fu¨rnkranz, J.: On the quest for optimal rule learning heuristics. Machine Learning 78, 343-379 (2010)
    • 8. Martens, D., Baesens, B., Fawcett, T.: Editorial survey: swarm intelligence for data mining. Machine Learning 82, 1-42 (2011)
    • 9. Otero, F., Freitas, A., Johnson, C.: A New Sequential Covering Strategy for Inducing Classification Rules with Ant Colony Algorithms. To appear in IEEE Trans. on Evolutionary Computation (2012)
    • 10. Parpinelli, R., Lopes, H., Freitas, A.: Data Mining with an Ant Colony Optimization Algorithm. IEEE Trans. on Evolutionary Computation 6(4), 321-332 (2002)
    • 11. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1993)
    • 12. Salama, K., Abdelbar, A.: Exploring Different Rule Quality Evaluation Functions in ACO-based Classification Algorithms. In: IEEE Symposium on Swarm Intelligence (SIS). pp. 1-8 (2011)
    • 13. Witten, I., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 3rd edn. (2011)
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

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