Document Type : Original/Review Paper


Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.


High dimensionality is the biggest problem when working with large datasets. Feature selection is a procedure for reducing the dimensionality of datasets by removing additional and irrelevant features; the most effective features in the dataset will remain, increasing the algorithms’ performance. In this paper, a novel procedure for feature selection is presented that includes a binary teaching learning-based optimization algorithm with mutation (BMTLBO). The TLBO algorithm is one of the most efficient and practical optimization techniques. Although this algorithm has fast convergence speed and it benefits from exploration capability, there may be a possibility of trapping into a local optimum. So, we try to establish a balance between exploration and exploitation. The proposed method is in two parts: First, we used the binary version of the TLBO algorithm for feature selection and added a mutation operator to implement a strong local search capability (BMTLBO). Second, we used a modified TLBO algorithm with the self-learning phase (SLTLBO) for training a neural network to show the application of the classification problem to evaluate the performance of the procedures of the method. We tested the proposed method on 14 datasets in terms of classification accuracy and the number of features. The results showed BMTLBO outperformed the standard TLBO algorithm and proved the potency of the proposed method. The results are very promising and close to optimal.


[1] M. F. Ahmad, N. A. M. Isa, W. H. Lim, and K. M. Ang, “Differential evolution: A recent review based on state-of-the-art works,” Alexandria Eng. J., 2021.
[2] M. Paniri, M. B. Dowlatshahi, and H. Nezamabadi-Pour, “MLACO: A multi-label feature selection algorithm based on ant colony optimization,” Knowledge-Based Syst., vol. 192, p. 105285, 2020.
[3] X.-Y. Liu, Y. Liang, S. Wang, Z.-Y. Yang, and H.-S. Ye, “A hybrid genetic algorithm with wrapper-embedded approaches for feature selection,” IEEE Access, vol. 6, pp. 22863–22874, 2018.
[4] A. Purohit, N. S. Chaudhari, and A. Tiwari, “Construction of classifier with feature selection based on genetic programming,” in IEEE Congress on Evolutionary Computation, 2010, pp. 1–5.
[5] L. Abualigah and A. Diabat, “Chaotic binary group search optimizer for feature selection,” Expert Syst. Appl., vol. 192, p. 116368, 2022.
[6] R. Kundu, S. Chattopadhyay, E. Cuevas, and R. Sarkar, “AltWOA: Altruistic Whale Optimization Algorithm for feature selection on microarray datasets,” Comput. Biol. Med., vol. 144, p. 105349, 2022.
[7] A. M. Ibrahim, M. A. Tawhid, and R. K. Ward, “A binary water wave optimization for feature selection,” Int. J. Approx. Reason., vol. 120, pp. 74–91, 2020.
[8] Z. A. Varzaneh, S. Hossein, S. E. Mood, and M. M. Javidi, “A new hybrid feature selection based on Improved Equilibrium Optimization,” Chemom. Intell. Lab. Syst., vol. 228, p. 104618, 2022.
[9] R. Ramasamy Rajammal, S. Mirjalili, G. Ekambaram, and N. Palanisamy, “Binary Grey Wolf Optimizer with Mutation and Adaptive K-nearest Neighbour for Feature Selection in Parkinson’s Disease Diagnosis,” Knowledge-Based Syst., vol. 246, p. 108701, 2022, doi:
[10] M. Taradeh et al., “An evolutionary gravitational search-based feature selection,” Inf. Sci. (Ny)., vol. 497, pp. 219–239, 2019, doi:
[11] R. Guha, M. Ghosh, A. Chakrabarti, R. Sarkar, and S. Mirjalili, “Introducing clustering based population in Binary Gravitational Search Algorithm for Feature Selection,” Appl. Soft Comput., vol. 93, p. 106341, 2020, doi:
[12] Z. Shojaee, S. A. Shahzadeh Fazeli, E. Abbasi, and F. Adibnia, “Feature Selection based on Particle Swarm Optimization and Mutual Information,” J. AI Data Min., vol. 9, no. 1, pp. 39–44, 2021.
[13] M. Tubishat et al., “Dynamic Salp swarm algorithm for feature selection,” Expert Syst. Appl., vol. 164, p. 113873, 2021, doi:
[14] I. Aljarah et al., “A dynamic locality multi-objective salp swarm algorithm for feature selection,” Comput. Ind. Eng., vol. 147, p. 106628, 2020, doi:
[15] M. Manonmani and S. Balakrishnan, “Feature Selection Using Improved Teaching Learning Based Algorithm on Chronic Kidney Disease Dataset,” Procedia Comput. Sci., vol. 171, pp. 1660–1669, 2020, doi: 10.1016/j.procs.2020.04.178.
[16] M. Allam and M. Nandhini, “Optimal feature selection using binary teaching learning based optimization algorithm,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 2, pp. 329–341, 2022, doi:
[17] S. Thawkar, “A hybrid model using teaching--learning-based optimization and Salp swarm algorithm for feature selection and classification in digital mammography,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 9, pp. 8793–8808, 2021.
[18] R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching--learning-based optimization: a novel method for constrained mechanical design optimization problems,” Comput. Des., vol. 43, no. 3, pp. 303–315, 2011.
[19] A. Taheri, K. RahimiZadeh, and R. V. Rao, “An efficient balanced teaching-learning-based optimization algorithm with individual restarting strategy for solving global optimization problems,” Inf. Sci. (Ny)., vol. 576, pp. 68–104, 2021.