Data mining with an ant colony optimization algorithm
Top Cited Papers
- 7 November 2002
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Evolutionary Computation
- Vol. 6 (4), 321-332
- https://doi.org/10.1109/tevc.2002.802452
Abstract
The paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.Keywords
This publication has 15 references indexed in Scilit:
- Ant colony optimization: a new meta-heuristicPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Ant Algorithms for Discrete OptimizationArtificial Life, 1999
- Simplifying decision trees: A surveyThe Knowledge Engineering Review, 1997
- Ant system: optimization by a colony of cooperating agentsIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996
- For Every Generalization Action, Is There Really an Equal and Opposite Reaction? Analysis of the Conservation Law for Generalization PerformancePublished by Elsevier BV ,1995
- Trading accuracy for simplicity in decision treesMachine Learning, 1994
- A Conservation Law for Generalization PerformancePublished by Elsevier BV ,1994
- Overfitting avoidance as biasMachine Learning, 1993
- Rule induction with CN2: Some recent improvementsPublished by Springer Science and Business Media LLC ,1991
- The CN2 induction algorithmMachine Learning, 1989