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An Ant Colony Algorithm for Classification Rule Discovery
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Rafael S. Parpinelli (Centro Federal de Educacao Tecnologica do Parano, Brazil), Heitor S. Lopes (Centro Federal de Educacao Tecnologica do Parano, Brazil) and Alex A. Freitas (Pontifica Universidade Catolica do Parana, Brazil) Copyright © 2002. 18 pages.
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This work proposes an algorithm for rule discovery called Ant-Miner (Ant Colony-Based Data Miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is based on recent research on the behavior of real ant colonies as well as in some data mining concepts. We compare the performance of Ant-Miner with the performance of the wellknown C4.5 algorithm on six public domain data sets. The results provide evidence that: (a) Ant-Miner is competitive with C4.5 with respect to predictive accuracy; and (b) the rule sets discovered by Ant-Miner are simpler (smaller) than the rule sets discovered by C4.5.
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Towards the Cross-Fertilization of Multiple Heuristics: Evolving Teams of Local Bayesian Learners (pages 117-142)
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Chapter 7
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Evolution of Spatial Data Templates for Object Classification (pages 143-156)
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An Ant Colony Algorithm for Classification Rule Discovery (pages 191-208)
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