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Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery
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DOI：10.1016/j.asoc.2012.07.026.png)
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摘要
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The cAnt-Miner algorithm is an Ant Colony Optimization (ACO) based technique for classification rule discovery in problem domains which include continuous attributes. In this paper, we propose several extensions to cAnt-Miner. The main extension is based on the use of multiple pheromone types, one for each class value to be predicted. In the proposed mu cAnt-Miner algorithm, an ant first selects a class value to be the consequent of a rule and the terms in the antecedent are selected based on the pheromone levels of the selected class value; pheromone update occurs on the corresponding pheromone type of the class value. The pre-selection of a class value also allows the use of more precise measures for the heuristic function and the dynamic discretization of continuous attributes, and further allows for the use of a rule quality measure that directly takes into account the confidence of the rule. Experimental results on 20 benchmark datasets show that our proposed extension improves classification accuracy to a statistically significant extent compared to cAnt-Miner, and has classification accuracy similar to the well-known Ripper and PART rule induction algorithms. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Classification rule discovery
Ant Colony Optimization
Biologically inspired computing
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IF:
6.6
论文数:
1.4W
被引数:
4.8W
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