# Ant colony classification mining algorithm based on pheromone attraction and exclusion

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## Abstract

Ant colony optimization algorithms have been applied successfully in classification rule mining. However, basic ant colony classification mining algorithms generally suffer from problems, such as premature convergence and falling into local optimum easily. Simultaneously, the classification mining algorithms use sequential covering strategy to discover rules, and the interaction between rules is less considered. In this study, a new ant colony classification mining algorithm based on pheromone attraction and exclusion (Ant-Miner\(_\mathrm{PAE}\)) is proposed, in which a new pheromone calculation method is designed and the search is guided by the new probability transfer formula. By contrast,the basic algorithm structure is modified, and the order of the iteration is adjusted. Thus, the problem of rule interaction is mitigated. Ant-Miner\(_\mathrm{PAE}\) can balance the relation of exploration and development of constructing rules, which can make the ants in the search process initially explore and develop in the later period. Our experiments, which use 12 publicly available data sets, show that the predictive accuracy obtained by Ant-Miner\(_\mathrm{PAE}\) implementing the proposed pheromone attraction and exclusion strategy is statistically significantly higher than the predictive accuracy of other rule induction classification algorithms, such as CN2, C4.5 rules, PSO/ACO2, Ant-Miner, and *c*Ant-Miner\(_\mathrm{PB}\). Furthermore, the rules discovered by Ant-Miner\(_\mathrm{PAE}\) are considerably simpler than those discovered by its counterparts.

## Keywords

Data mining Classification rule Ant colony algorithm Pheromone attraction and exclusion## Notes

### Acknowledgments

This work was partially supported by Science and Technology Project of Guangdong Province of China (Grant Nos. 2015A020209119 and 2014A020208087), National Natural Science Foundation of China (Grant No. 61573157), and Fund of Natural Science Foundation of Guangdong Province of China (Grant No. S2013040015755). The authors also gratefully acknowledge the reviewers for their helpful comments and suggestions that helped to improve the presentation.

### Compliance with ethical standards

### Conflict of interest

The authors declare that they have no conflict of interest.

### Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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