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Exploring Different Functions for Heuristics, Discretization, and Rule Quality Evaluation in Ant-Miner

Salama, Khalid M. and Otero, Fernando E.B. (2012) Exploring Different Functions for Heuristics, Discretization, and Rule Quality Evaluation in Ant-Miner. In: Swarm Intelligence 8th International Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 344-345. ISBN 978-3-642-32649-3. E-ISBN 978-3-642-32650-9. (doi:10.1007/978-3-642-32650-9_38) (KAR id:71273)


Data mining is a process that supports knowledge discovery by finding hidden patterns, associations and constructing analytical models from databases. Classification is one of the widely studied data mining tasks in which the aim is to discover, from labelled cases, a model that can be used to predict the class of unlabelled cases. Ant-Miner, proposed by Parpinelli et al. [3], is the first ACO algorithm for discovering classification rules. Ant-Miner has been shown to be competitive with well-known classification algorithms, in terms of producing comprehensible model with high predictive accuracy. Therefore, there has been an increasing interest in improving the Ant-Miner algorithm [1]

Item Type: Book section
DOI/Identification number: 10.1007/978-3-642-32650-9_38
Uncontrolled keywords: Swarm Intelligence, Rule Construction, Heuristic Information, Dynamic Discretization, Hide Pattern
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 19 Dec 2018 02:30 UTC
Last Modified: 16 Nov 2021 10:25 UTC
Resource URI: (The current URI for this page, for reference purposes)

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Otero, Fernando E.B..

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