Abstract
Feature selection technique has an important role in the elimination of unrelated features and noises from the high-dimensional data. It simplifies and enhances the quality of dataset by selecting salient features. Good feature selection algorithm leads to accurate classification. Feature selection of high-dimensional dataset addresses the problem with redundancy, accuracy, and computational complexity. Ant colony optimization (ACO) is a modern algorithm for feature selection. It is an evolutionary algorithm inspired by the foraging behavior of ants. This paper proposes the technique of weighted visibility graph and ACO method for feature extraction and feature selection. In this method, high-dimensional dataset is converted into the complex network and after extracting eight well-suited features from the dataset, feature selection is performed. Naive Bayes method is utilized to classify the selected features. Experimental results indicate that the classification accuracy is more accurate using the proposed method.
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References
Kashef, S., Nezamabadi, P.H.: Introducing a new version of binary ant colony algorithm to solve the problem of feature selection. Sci. Inf. Database 12(2), 127–134 (2015)
Dadaneh, B.Z., Markid, H.Y., Zakerolhosseini, A.: Unsupervised probabilistic feature selection using ant colony optimization. Expert Syst. Appl. 53, 27–42 (2016)
Wan, Y., Wang, M., Ye, Z., Lai, X.: A feature selection method based on modified binary coded ant colony optimization algorithm. Appl. Soft Comput. 49, 248–258 (2016)
Akay, M.F.: Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst. Appl. 36, 3240–3247 (2009)
Deriche, M.: Feature selection using ant colony optimization. In: 6th International Multi-conference Systems, Signals and Devices. SSD’09, pp. 1–4 (2009)
Moradi, P., Rostami, M.: Integration of graph clustering with ant colony optimization for feature selection. Knowl. Based Syst. 84, 144–161 (2015)
Dorigo, M., Stutzle, T.: Ant colony optimization. Encylopedia of Machine Learning (2010)
Ariyasingha, I., Fernando, T.: Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem. Swarm Evol. Comput. 23, 11–26 (2015)
Abd-Alsabour, N., Randall, M.: Feature selection for classification using an ant colony system. In: Sixth IEEE International Conference on e–Science Workshops (2010)
Mohammed, S.K., Deeba, F., Bui, F.M., Wahid, K.A.: Feature selection using modified ant colony optimization for wireless capsule endoscopy. In: Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual, pp. 1–4 (2016)
Menéndez, H.D., Otero, F.E., Camacho, D.: Medoid-based clustering using ant colony optimization. Swarm Intell. 10, 123–145 (2016)
Balasaraswathi, V.R., Sugumaran, M., Hamid, Y.: Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. J. Commun. Inf. Netw. 2, 107–119 (2017)
Schiezaro, M., Pedrini, H.: Data feature selection based on Artificial Bee Colony algorithm. EURASIP J. Image Video Process. 47 (2013)
Li, Y., Wang, G., Chen, H., Shi, L., Qin, L.: An ant colony optimization based dimension reduction method for high-dimensional datasets. J. Bionic Eng. 10, 231–241 (2013)
Moosa, J.M., Shakur, R., Kaykobad, M., Rahman, M.S.: Gene selection for cancer classification with the help of bees. BMC Med. Genomics 9 (2016)
Shunmugapriya, P., Kanmani, S.: A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid). Swarm Evol. Comput. 36, 27–36 (2017)
Supriya, S., Siuly, S., Wang, H., Cao, J., Zhang, Y.: Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access 4, 6554–6566 (2016)
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: the visibility graph. Proc. Natl. Acad. Sci. U. S. A. 105(13), 4972–4975 (2008)
Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997)
Lu, H., Chen, J., Yan, K., Jin, Q., Xue, Y., Gao, Z.: A hybrid feature selection algorithm for gene expression data classification. Neurocomputing 256, 56–62 (2017)
Zorarpacı, E., Özel, S.A.: A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Syst. Appl. 62(C), 91–103 (2016)
Bhateja, V., et al.: Ant colony optimization based anisotropic diffusion for despeckling of SAR images. In: International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision making, pp. 389–396. Springer, Cham (2016)
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Sekhar, L.C., Vijayakumar, R. (2021). Feature Selection Using Ant Colony Optimization and Weighted Visibility Graph. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_3
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DOI: https://doi.org/10.1007/978-981-15-5788-0_3
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