James Smaldon
ABSTRACT
The Ant-Miner algorithm, first proposed by Parpinelli and colleagues, applies an ant colony optimization heuristic to the classification task of data mining to discover an ordered list of classification rules. In this paper we present a new version of the Ant-Miner algorithm, which we call Unordered Rule Set Ant-Miner, that produces an unordered set of classification rules. The proposed version was evaluated against the original Ant-Miner algorithm in six public-domain datasets and was found to produce comparable results in terms of predictive accuracy. However, the proposed version has the advantage of discovering more modular rules, i.e., rules that can be interpreted independently from other rules - unlike the rules in an ordered list, where the interpretation of a rule requires knowledge of the previous rules in the list. Hence, the proposed version facilitates the interpretation of discovered knowledge, an important point in data mining.
- M. Dorigo, A. Colorni and V. Maniezzo, "The Ant System: optimization by a colony of cooperating agents," IEEE Transactions on Systems, Man, and Cybernetics-Part B, vol. 26, no. 1, pp. 29--41, 1996. Google Scholar
Digital Library
- B. Liu, H. A. Abbass, B. Mckay. Classi?cation rule discovery with ant colony optimization. Proceeding of the IEEE/WIC International Conference on Intelligent Agent Technology, Beijing, China (2003), pp. 83--88. Google ScholarDigital Library
- M. P. Oakes, "Ant Colony Optimisation for Stylometry: The Fedaralist Papers." International Conference on Recent Advances in Soft Computing, November 2004.Google Scholar
- Ziqiang Wang, Boqin Feng, Classification Rule Mining with an Improved Ant Colony Algorithm, Lecture Notes in Computer Science, Volume 3339, Jan 2004, pp. 357--367. Google ScholarDigital Library
- R. S. Parpinelli, H. S. Lopes, and A. A. Freitas. Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computing 6(4), 2002, pp. 321--332. Google ScholarDigital Library
- J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993. Google ScholarDigital Library
- I. H. Witten and E. Frank. Data Mining: practical machine learning tools and techniques. 2nd Edition. Morgan Kaufmann, 2005. Google ScholarDigital Library
- R. S. Parpinelli, H. S. Lopes and A. A. Freitas. An Ant Colony Algorithm for Classification Rule Discovery. In: H. A. Abbass, R. A. Sarker, C. S. Newton. (Eds.) Data Mining: a Heuristic Approach, pp. 191--208. London: Idea Group Publishing, 2002.Google ScholarCross Ref
- P. Clark and R. Boswell. Rule induction with CN2: some recent improvements. Proc. European Working Session on Learning (EWSL-91), LNAI 482, pp. 151--163. Springer, 1991. Google ScholarDigital Library
Index Terms
- A new version of the ant-miner algorithm discovering unordered rule sets
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