Improving the cAnt-MinerPB Classification Algorithm

Medland, Matthew and Otero, Fernando E.B. and Freitas, Alex A. (2012) Improving the cAnt-MinerPB Classification Algorithm. In: Swarm Intelligence. (doi: (Full text available)

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Ant Colony Optimisation (ACO) has been successfully applied to the classification task of data mining in the form of Ant-Miner. A new extension of Ant-Miner, called cAnt-MinerPB, uses the ACO procedure in a different fashion. The main difference is that the search in cAnt-MinerPB is optimised to find the best list of rules, whereas in Ant-Miner the search is optimised to find the best individual rule at each step of the sequential covering, producing a list of best rules. We aim to improve cAnt-MinerPB in two ways, firstly by dynamically finding the rule quality function which is used while the rules are being pruned, and secondly improving the rule-list quality function which is used to guide the search. We have found that changing the rule quality function has little effect on the overall performance, but that by improving the rule-list quality function we can positively affect the discovered lists of rules.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Fernando Otero
Date Deposited: 21 Sep 2012 09:49 UTC
Last Modified: 27 Jun 2017 21:48 UTC
Resource URI: (The current URI for this page, for reference purposes)
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