Using Ant Colony Optimization to Build Cluster-Based Classification Systems
Learning cluster-based classification systems is the process of partitioning a training set into data subsets (clusters), and then building a local classifier for each data cluster. The class of a new instance is predicted by first assigning the instance to its nearest cluster, and then using that cluster’s local classification model to predict the instance’s class. In this paper, we use the Ant Colony Optimization (ACO) meta-heuristic to optimize the data clusters based on a given classification algorithm in an integrated cluster-with-learn manner. The proposed ACO algorithms use two different clustering solution representation approaches: instance-based and medoid-based, where in the latter the number of clusters is optimized as part of the ACO algorithm’s execution. In our experiments, we employ three widely-used classification algorithms, k-nearest neighbours, Ripper, and C4.5, and evaluate performance on 30 UCI benchmark datasets. We compare the ACO results to the traditional c-means clustering algorithm, where the data clusters are built prior to learning the local classifiers.
KeywordsAnt Colony Optimization (ACO) Data mining Classification Clustering Cluster-based classification system
Partial support of a grant from the Brandon University Research Council is gratefully acknowledged.
- 1.Abdelbar, A.M., Salama, K.M.: Clustering with the ACOR algorithm. In: Swarm Intelligence, LNCS, vol. 9882, pp. 210–222 (2016)Google Scholar
- 6.Liu, X.Y., Fu, H.: An effective clustering algorithm with ant colony. J. Comput. 5, 598–605 (2010)Google Scholar
- 10.Otero, F.E., Freitas, A.A., Johnson, C.G.: Handling continuous attributes in ant colony classification algorithms. In: IEEE Symposium on Computational Intelligence in Data Mining (CIDM 2009), pp. 225–231 (2009)Google Scholar
- 14.Salama, K.M., Abdelbar, A.M., Anwar, I.M.: Data reduction for classification with ant colony optimization. Intelligent Data Analysis (2016, to appear)Google Scholar
- 16.Salama, K.M., Abdelbar, A.M., Helal, A.Z., Freitas, A.A.: Instance-based classification with ant colony optimization. Intelligent Data Analysis (accepted, 2016)Google Scholar
- 17.Salama, K.M., Freitas, A.A.: Clustering-based Bayesian multi-net classifier construction with ant colony optimization. In: IEEE Congress on Evolutionary Computation (IEEE CEC), pp. 3079–3086 (2013)Google Scholar
- 21.Salama, K.M., Freitas, A.A.: Ant colony algorithms for constructing Bayesian multi-net classifiers. Intell. Data Anal. 19(2), 233–257 (2015)Google Scholar
- 22.Salama, K.M., Otero, F.E.: Learning multi-tree classification models with ant colony optimization. In: 6th International Conference on Evolutionary Computation Theory and Applications (ECTA 2014), pp. 38–48 (2014)Google Scholar
- 26.Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 2nd edn. Addison Wesley, Reading (2005)Google Scholar
- 29.Xu, R., Wunsch, D.: Clustering. Wiley-IEEE Press, Hoboken (2009)Google Scholar