Abstract
This work describes 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 and 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: (a) Ant-Miner is competitive with CN2 with respect to predictive accuracy; and (b) The rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.
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Parpinelli, R.S., Lopes, H.S., Freitas, A.A. (2002). Mining Comprehensible Rules from Data with an Ant Colony Algorithm. In: Bittencourt, G., Ramalho, G.L. (eds) Advances in Artificial Intelligence. SBIA 2002. Lecture Notes in Computer Science(), vol 2507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36127-8_25
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DOI: https://doi.org/10.1007/3-540-36127-8_25
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