Abstract
Feature selection has been proven to be an effective method for handling large amounts of data, which together with the parameter settings of the classifier determines the performance of the classifier. However, many studies have considered the two separately, ignoring the intrinsic connection between them. Thus, in this work, we formulate a joint feature selection and parameters optimization problem, which is NP-hard and mixed-variable structured. Then we propose an improved binary honey badger algorithm (IBHBA) to solve the formulated problem. First, a novel initialization strategy based on the fast correlation-based filter (FCBF) method is proposed to generate promising initial solutions. Second, IBHBA introduces a local search factor based on simulated annealing (SA), a crossover operator based on tournament selection, and a mutation mechanism to improve the performance of conventional HBA. Finally, a binary mechanism is adopted to make it suitable for the feature selection problem. Experiments conducted in 27 public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R.K., Kaur, B., Sharma, S.: Quantum based whale optimization algorithm for wrapper feature selection. Appl. Soft Comput. 89, 106092 (2020)
Blake, C.: UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html (1998)
Cervantes, J., García-Lamont, F., Rodríguez-Mazahua, L., López Chau, A.: A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408, 189–215 (2020)
Chen, H., Zhang, Z., Yin, W., Zhao, C., Wang, F., Li, Y.: A study on depth classification of defects by machine learning based on hyper-parameter search. Measurement 189, 110660 (2022)
Dai, Y., Zhao, P.: A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization. Appl. Energy 279, 115332 (2020)
Deng, Y., Guan, D., Chen, Y., Yuan, W., Ji, J., Wei, M.: SAR-ShipNet: SAR-Ship detection neural network via bidirectional coordinate attention and multi-resolution feature fusion. In: Proceedings IEEE ICASSP, pp. 3973–3977 (2022)
Dhal, P., Azad, C.: A comprehensive survey on feature selection in the various fields of machine learning. Appl. Intell. 52(4), 4543–4581 (2022)
Elgamal, Z.M., Sabri, A.Q.M., Tubishat, M., Tbaishat, D., Makhadmeh, S.N., Alomari, O.A.: Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field. IEEE Access 10, 51428–51446 (2022)
Ghanem, W.A.H.M., et al.: Cyber intrusion detection system based on a multiobjective binary bat algorithm for feature selection and enhanced bat algorithm for parameter optimization in neural networks. IEEE Access 10, 76318–76339 (2022)
Ghosh, S., Dasgupta, A., Swetapadma, A.: A study on support vector machine based linear and non-linear pattern classification. In: Proceedings IEEE ICISS, pp. 24–28 (2019)
Guan, D., et al.: A novel class noise detection method for high-dimensional data in industrial informatics. IEEE Trans. Ind. Informatics 17(3), 2181–2190 (2021)
Hashim, F.A., Houssein, E.H., Hussain, K., Mabrouk, M.S., Al-Atabany, W.: Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math. Comput. Simul. 192, 84–110 (2022)
Karn, R.R.P., et al.: A feature and parameter selection approach for visual domain adaptation using particle swarm optimization. In: Proceedings IEEE CEC, pp. 1–7. IEEE (2022)
Karthikeyan, R., Alli, P.: Feature selection and parameters optimization of support vector machines based on hybrid glowworm swarm optimization for classification of diabetic retinopathy. J. Med. Syst. 42(10), 1–11 (2018)
Li, A., Xue, B., Zhang, M.: Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection. Inf. Sci. 523, 245–265 (2020)
Mehedi, I.M., et al.: Optimal feature selection using modified cuckoo search for classification of power quality disturbances. Appl. Soft Comput. 113(Part), 107897 (2021)
Mirjalili, S., Lewis, A.: S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evol. Comput. 9, 1–14 (2013)
Ren, W., Ma, D., Han, M.: Multivariate time series predictor with parameter optimization and feature selection based on modified binary salp swarm algorithm. IEEE Trans. Indust. Inf. 19, 6150–6159 (2022)
Sakri, S.B., Rashid, N.B.A., Zain, Z.M.: Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access 6, 29637–29647 (2018)
Too, J., Sadiq, A.S., Mirjalili, S.M.: A conditional opposition-based particle swarm optimisation for feature selection. Connect. Sci. 34(1), 339–361 (2022)
Tubishat, M., et al.: Dynamic salp swarm algorithm for feature selection. Expert Syst. Appl. 164, 113873 (2021)
Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Fawcett, T., Mishra, N. (eds.) Proceedings ICML, pp. 856–863. AAAI Press (2003)
Zhang, Y., Liu, R., Wang, X., Chen, H., Li, C.: Boosted binary Harris Hawks optimizer and feature selection. Eng. Comput. 37(4), 3741–3770 (2021)
Zhou, T., Lu, H., Wenwen, W., Xia, Y.: GA-SVM based feature selection and parameter optimization in hospitalization expense modeling. Appl. Soft Comput. 75, 323–332 (2019)
Zou, L., Zhou, S., Li, X.: An efficient improved greedy Harris Hawks optimizer and its application to feature selection. Entropy 24(8), 1065 (2022)
Acknowledgements
This work is supported in part by the National Key Research and Development Program of China (2022YFB4500600), in part by the National Natural Science Foundation of China (61872158, 62002133, 62172186, 62272194), in part by the Science and Technology Development Plan Project of Jilin Province (20200201166JC, 20190701019GH, 20190701002GH), in part by Graduate Innovation Fund of Jilin University (2022028, 2022155, 2023CX013), and in part by the Excellent Young Talents Program for Department of Science and Technology of Jilin Province (Grant 20190103051JH).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wei, Z. et al. (2023). Joint Feature Selection and Classifier Parameter Optimization: A Bio-Inspired Approach. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14117. Springer, Cham. https://doi.org/10.1007/978-3-031-40283-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-40283-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40282-1
Online ISBN: 978-3-031-40283-8
eBook Packages: Computer ScienceComputer Science (R0)