Abstract The performance of the machine learning models mainly relies on the key features available in the training dataset. Feature selection is a significant job for pattern recognition for finding an important group of features to build classification models with a minimum number of features. Feature selection with optimization algorithms will improve the prediction rate of the classification models. But, tuning the controlling parameters of the optimization algorithms is a challenging task. In this paper, we present a wrapper-based model called Feature Selection with Integrative Teaching Learning Based Optimization (FS-ITLBO), which uses multiple teachers to select the optimal set of features from feature space. The goal of the proposed algorithm is to search the entire solution space without struck in the local optima of features. Moreover, the proposed method only utilizes teacher count parameter along with the size of the population and a number of iterations. Various classification models have been used for finding the fitness of instances in the population and to estimate the effectiveness of the proposed model. The robustness of the proposed algorithm has been assessed on Wisconsin Diagnostic Breast Cancer (WDBC) as well as Parkinson’s Disease datasets and compared with different wrapper-based feature selection techniques, including genetic algorithm and Binary Teaching Learning Based Optimization (BTLBO). The outcomes have confirmed that FS-ITLBO model produced the best accuracy with the optimal subset of features.

References

[1] Allam M. and Nandhini M., “A Study on Optimization Techniques in Feature Selection for Medical Image Analysis,” International Journal on Computer Science and Engineering, vol. 9, no. 3, pp. 75-82, 2017. Wrapper based Feature Selection using Integrative Teaching Learning Based ... 893

[2] Allam M. and Nandhini M., “Feature Optimization using Teaching Learning Based Optimization for Breast Disease Diagnosis,” International Journal of Recent Technology and Engineering, vol. 7, no. 4, pp. 78-85, 2018.

[3] Allam M. and Nandhini M., “Optimal Feature Selection Using Binary Teaching Learning Based Optimization Algorithm,” Journal of King Saud University-Computer and Information Sciences, pp.1-13, 2018.

[4] Arora S. and Anand P., “Binary Butterfly Optimization Approaches for Feature Selection,” Expert Systems with Applications, vol. 116, pp. 147-160, 2019.

[5] Bhardwaj H., Sakalle A., Tiwari A., Verma M., and Bhardwaj A., “Breast Cancer Diagnosis using Simultaneous Feature Selection and Classification: A Genetic Programming Approach,” in Proceedings of IEEE Symposium Series on Computational Intelligence, Bangalore, pp. 2186-2192, 2018.

[6] Chandrasekhar A. and Rani T., “Storage and Retrieval of Large Data Sets: Dimensionality Reduction and Nearest Neighbour Search,” in Proceedings of International Conference on Contemporary Computing, Noida, pp. 262-272, 2012.

[7] Dua D. and Graff C., “UCI Machine Learning Repository: Breast Cancer Wisconsin Data Set (Diagnostic),” Irvine, CA: University of California, 2019.

[8] Guyon I. and Elisseeff A., “An Introduction to Variable and Feature Selection,” Journal of Machine Learning Research, vol. 3, pp. 1157- 1182, 2003.

[9] Hafez A., Zawbaa H., Emery E., Mahmoud H., and Hassanien A., “An Innovative Approach for Feature Selection Based on Chicken Swarm Optimization,” in Proceedings of 7th International Conference of Soft Computing and Pattern Recognition, Fukuoka, pp. 19-24, 2015.

[10] Hossam A., Harb H., and Kader H., “A Sub- Optimum Feature Selection Algorithm for Effective Breast Cancer Detection Based on Particle Swarm Optimization,” IOSR Journal of Electronics and Communication Engineering, vol. 13, no. 3, pp. 01-12, 2018.

[11] Hua J., Tembe W., and Dougherty E., “Performance of Feature-Selection Methods in The Classification of High-Dimension Data,” Pattern Recognition, vol. 42, no. 3, pp. 409-424, 2009.

[12] Janecek A., Gansterer W., Demel M., and Ecker G., “On the Relationship Between Feature Selection and Classification Accuracy,” Proceedings of Machine Learning Research, vol. 4, pp. 90-105, 2008.

[13] Kiziloz H., Deniz A., Dokeroglu T., and Cosar A., “Novel Multiobjective TLBO Algorithms for The Feature Subset Selection Problem,” Neurocomputing, vol. 306, pp. 94-107, 2018.

[14] Kohavi R. and John G., “Wrappers for Feature Subset Selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273-324, 1997.

[15] Li J. and Liu H., “Challenges of Feature Selection for Big Data Analytics,” IEEE Intelligent Systems, vol. 32, no. 2, pp. 9-15, 2017.

[16] Mafarja M., Eleyan D., Jaber I., Hammouri A., and Mirjalili S., “Binary Dragonfly Algorithm for Feature Selection,” in Proceedings of International Conference on New Trends in Computing Sciences, Amman, pp. 12-17, 2017.

[17] Moradi P. and Gholampour M., “A Hybrid Particle Swarm Optimization for Feature Subset Selection by Integrating a Novel Local Search Strategy,” Applied Soft Computing, vol. 43, pp. 117-130, 2016.

[18] Oluleye B., Leisa A., Leng J., and Dean D., “A Genetic Algorithm-Based Feature Selection,” International Journal of Electronics Communication and Computer Engineering, vol. 5, no. 4, pp. 899-905, 2014.

[19] Panda M., “Elephant Search Optimization Combined with The Deep Neural Network for Microarray Data Analysis,” Journal of King Saud University-Computer and Information Sciences, 2017.

[20] Rajamohana S. and Umamaheswari K., “Hybrid Approach of Improved Binary Particle Swarm Optimization and Shuffled Frog Leaping for Feature Selection,” Computers and Electrical Engineering, vol. 67, pp. 497-508, 2018.

[21] Rao R., “Review of Applications of TLBO Algorithm and A Tutorial for Beginners to Solve The Unconstrained and Constrained Optimization Problems,” Decision Science Letters, vol. 5, no. 1, pp. 1-30, 2016.

[22] Rao R., Teaching Learning Based Optimization Algorithm and its Engineering Applications, Springer International Publishing, 2016.

[23] Rao R. and Patel V., “An Improved Teaching- Learning-Based Optimization Algorithm for Solving Unconstrained Optimization Problems,” Scientia Iranica, vol. 20, no. 3, pp. 710-720, 2013.

[24] Rao R., Savsani V., and Vakharia D., “Teaching- Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011.

[25] Rodrigues D., Pereira L., Nakamura R., Costa A., Yang X., Souza A., and Papa J., “A Wrapper Approach for Feature Selection Based on Bat Algorithm and Optimum-Path Forest,” Expert 894 The International Arab Journal of Information Technology, Vol. 17, No. 6, November 2020 Systems with Applications, vol. 41, no. 5, pp. 2250-2258, 2014.

[26] Sakar C., Serbes G., Gunduz A., Tunc H., Nizam H., Sakar B., Tutuncu M., Aydin T., Isenkul M., and Apaydin H., “A Comparative Analysis of Speech Signal Processing Algorithms for Parkinson’s Disease Classification and the Use of The Tunable Q-Factor Wavelet Transform,” Applied Soft Computing, vol. 74, 2019.

[27] Sayed G., Hassanien A., and Azar A., “Feature Selection Via A Novel Chaotic Crow Search Algorithm,” Neural Computing and Applications, vol. 31, no. 1, pp. 171-188, 2019.

[28] Sayed G., Khoriba G., and Haggag M., “A Novel Chaotic Salp Swarm Algorithm for Global Optimization and Feature Selection,” Applied Intelligence, vol. 48, no. 10, pp. 3462-3481, 2018.

[29] Selvarajan D., Jabar A., and Ahmed I., “Comparative Analysis of PSO and ACO Based Feature Selection Techniques for Medical Data Preservation,” The International Arab Journal of Information Technology, vol. 16, no. 4, pp. 731- 736, 2019.

[30] Shahbeig S., Helfroush M., and Rahideh A., “A Fuzzy Multi-Objective Hybrid TLBO-PSO Approach to Select the Associated Genes with Breast Cancer,” Signal Processing, vol. 131, pp. 58-65, 2016.

[31] Sridevi T. and Murugan A., “A Novel Feature Selection Method for Effective Breast Cancer Diagnosis and Prognosis,” International Journal of Computer Applications., vol. 88, no. 11, pp. 28-33, 2014.

[32] Tan K., Teoh E., Yu Q., and Goh K., “A Hybrid Evolutionary Algorithm for Attribute Selection in Data Mining,” Expert Systems with Applications, vol. 36, no. 4, pp. 8616-8630, 2009.

[33] Tuo S., Yong L., Deng F., Li Y., Lin Y., and Lu Q., “HSTLBO: A Hybrid Algorithm Based on Harmony Search and Teaching-Learning Based Optimization for Complex High Dimensional Optimization Problems,” PLoSONE, vol. 12, no. 4, pp. 1-23, 2017. Mohan Allam, Research Scholar in the Department of Computer Science, Pondicherry University, Puducherry, India, and working as Assistant Professor at Shri Vishnu Engineering College for Women. His research interests include Soft Computing and Image Processing. Nandhini Malaiyappan, Associate Professor in the Department of Computer Science, Pondicherry University, Puducherry, India. Her research interests include Artificial Intelligence, Software Engineering, Evolutionary Algorithms, and Combinatorial Problem Optimization.