Abstract
Accurate classification of examples depends upon identification of informative attributes and removal of redundant attributes. Attribute selection helps reduce the noise and increase classification accuracy. In computational biology and bioinformatics, feature selection facilitates identification of most relevant attributes thereby facilitating valuable domain information. In this project, we have employed a synergistic filter-wrapper methodology for simultaneous classification and attribute selection. Ant colony optimization employs correlation and Gini ranking information along with pheromone learning, for providing better and better attributes as iterations proceed. The robust random forest classifier employed classifies the data to maximize fivefold accuracy. We evaluated the performance of our algorithm with eight benchmark datasets.
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References
Liu H, Tu L (2004) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502
Beniwal S, Arora J (2012) Classification and feature selection techniques in data mining. IJERT 1(6)
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1:28–39
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of sixth international symposium micro machine human science, MHS 95, pp 39–43
Rodrigues D, Pereira LA, Almeida TN, Papa JP, Souza AN, Ramos CC, Yang XS (2013) BCS: a binary cuckoo search algorithm for feature selection. In: 2013 IEEE international symposium on circuits and systems (ISCAS). IEEE, pp 465–468
Rodrigues D, Pereira LA, Nakamura RY, Costa KA, Yang XS, Souza AN, Papa JP (2014) A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Exp Syst Appl 41(5):2250–2258
Barani F, Mirhosseini M, Nezamabadi-Pour H (2017) Application of binary quantum-inspired gravitational search algorithm in feature subset selection. Appl Intell 47(2):304–318
Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 18(260):302–312
Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 8(172):371–381
Pandey AC, Rajpoot DS, Saraswat M (2020) Feature selection method based on hybrid data transformation and binary binomial cuckoo search. J Ambient Intel Human Comput 11(2):719–738
Shekhawat SS, Sharma H, Kumar S, Nayyar A, Qureshi B (2021) bSSA: binary salp swarm algorithm with hybrid data transformation for feature selection. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3049547
Kumar S, Sharma B, Sharma VK, Poonia RC (2018) Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm. Evol Intel 1–12. https://doi.org/10.1007/s12065-018-0186-9
Kumar S, Sharma B, Sharma VK, Sharma H, Bansal JC (2018) Plant leaf disease identification using exponential spider monkey optimization. Sustain Comput Inf Syst 28. https://doi.org/10.1016/j.suscom.2018.10.004
Kashef S, Nezamabadi-pour H (2015) An advanced ACO algorithm for feature subset selection. Neurocomputing 147:271–279
Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst 191:105190
Wan Y, Wang M, Ye Z, Lai X (2016) A feature selection method based on modified binary coded ant colony optimization algorithm. Appl Soft Comput 49:248–258
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Joshi, T., Lahorkar, A., Tikhe, G., Bhosale, H., Sane, A., Valadi, J.K. (2021). An Improved Ant Colony Optimization with Correlation and Gini Importance for Feature Selection. In: Sharma, H., Gupta, M.K., Tomar, G.S., Lipo, W. (eds) Communication and Intelligent Systems. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1089-9_50
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DOI: https://doi.org/10.1007/978-981-16-1089-9_50
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