A novel chaotic binary butterfly optimization algorithm based feature selection model for classification of autism spectrum disorder

Ramakrishnan, Anandkumar ; Ramalingam, Rajakumar ; Ramalingam, Padmanaban ; Ravi, Vinayakumar  ; Alahmadi, Tahani Jaser ; Maidin, Siti Sarah

International Journal of Applied Mathematics and Computer Science, Tome 34 (2024) no. 4, pp. 647-660.

Résumé

Autism spectrum disorder (ASD) issues formidable challenges in early diagnosis and intervention, requiring efficient methods for identification and treatment. By utilizing machine learning, the risk of ASD can be accurately and promptly evaluated, thereby optimizing the analysis and expediting treatment access. However, accessing high dimensional data degrades the classifier performance. In this regard, feature selection is considered an important process that enhances the classifier results. In this paper, a chaotic binary butterfly optimization algorithm based feature selection and data classification (CBBOAFS-DC) technique is proposed. It involves, preprocessing and feature selection along with data classification. Besides, a binary variant of the chaotic BOA (CBOA) is presented to choose an optimal set of a features. In addition, the CBBOAFS-DC technique employs bacterial colony optimization with a stacked sparse auto-encoder (BCO-SSAE) model for data classification. This model makes use of the BCO algorithm to optimally adjust the ‘weight’ and ‘bias’ parameters of the SSAE model to improve classification accuracy. Experiments show that the proposed scheme offers better results than benchmarked methods.

Détail

Keywords: data classification, feature selection, metaheuristics, machine learning, autism spectrum disorder
Mots-clés : klasyfikacja danych, wybór funkcji, metaheurystyka, uczenie maszynowe, spektrum autyzmu

Bibliographie

[1] Al-Muhanna, M.K., Alghamdi, A.A., Alrfaei, B.M., Afzal, M., Al-Subaiee, R. and Haddadi, R. (2024). An attention-based hybrid optimized residual memory network (AHRML) method for autism spectrum disorder (ASD) detection, Journal of Disability Research 3(3): 20240030.

[2] Alhafedh, M.A.A. and Qasim, O.S. (2019). Two-stage gene selection in microarray dataset using fuzzy mutual information and binary particle swarm optimization, Indian Journal of Forensic Medicine & Toxicology 13(4): 1162-1171.

[3] Almars, A.M., Badawy, M. and Elhosseini, M.A. (2023). ASD2-TL* GTO: Autism spectrum disorders detection via transfer learning with gorilla troops optimizer framework, Heliyon 9(11): e21530.

[4] Arora, S. and Anand, P. (2019). Binary butterfly optimization approaches for feature selection, Expert Systems with Applications 116: 147-160.

[5] Banaie-Dezfouli, M., Nadimi-Shahraki, M.H. and Beheshti, Z. (2023). BE-GWO: Binary extremum-based grey wolf optimizer for discrete optimization problems, Applied Soft Computing 146: 110583.

[6] Chen, R.-C., Dewi, C., Huang, S.-W. and Caraka, R.E. (2020). Selecting critical features for data classification based on machine learning methods, Journal of Big Data 7(1): 52-78.

[7] Czmil, S., Kluska, J. and Czmil, A. (2024). An empirical study of a simple incremental classifier based on vector quantization and adaptive resonance theory, International Journal of Applied Mathematics and Computer Science 34(1): 149-165, DOI: 10.61822/amcs-2024-0011.

[8] Dong, H., Sun, J., Sun, X. and Ding, R. (2020). A many-objective feature selection for multi-label classification, Knowledge-Based Systems 208: 106456.

[9] Duan, L., Liu, J., Yin, H., Wang, W., Liu, L., Shen, J. and Wang, Z. (2022). Dynamic changes in spatiotemporal transcriptome reveal maternal immune dysregulation of autism spectrum disorder, Computers in Biology and Medicine 151: 106334.

[10] Gaspar, A., Oliva, D., Hinojosa, S., Aranguren, I. and Zaldivar, D. (2022). An optimized kernel extreme learning machine for the classification of the autism spectrum disorder by using gaze tracking images, Applied Soft Computing 120: 108654.

[11] Helen Josephine, V., Vedaiyan, R., Xavier, V., Winston, J., Jegatheesan, A., Lakshmi, D. and Raj, J.S. (2023). Hybrid bacterial foraging optimization with sparse autoencoder for energy systems, Computer Systems Science and Engineering 45(1): 701-714.

[12] Hirota, T. and King, B.H. (2023). Autism spectrum disorder: A review, Jama 329(2): 157-168.

[13] Jiménez-Cordero, A., Morales, J.M. and Pineda, S. (2021). A novel embedded min-max approach for feature selection in nonlinear support vector machine classification, European Journal of Operational Research 293(1): 24-35.

[14] Li, G., Han, D., Wang, C., Hu, W., Calhoun, V.D. and Wang, Y.-P. (2020). Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia, Computer Methods and Programs in Biomedicine 183: 105073.

[15] Li, J., Chen, Z., Li, G., Ouyang, G. and Li, X. (2022a). Automatic classification of ASD children using appearance-based features from videos, Neurocomputing 470: 40-50.

[16] Li, L., Wang, Y., Xu, Y. and Lin, K.-Y. (2022b). Meta-learning based industrial intelligence of feature nearest algorithm selection framework for classification problems, Journal of Manufacturing Systems 62: 767-776.

[17] Liu, Y., Ju, S., Wang, J. and Su, C. (2020). A new feature selection method for text classification based on independent feature space search, Mathematical Problems in Engineering 2020(1): 6076272.

[18] Loganathan, S., Geetha, C., Nazaren, A.R. and Fernandez, M.H.F. (2023). Autism spectrum disorder detection and classification using chaotic optimization based Bi-GRU network: An weighted average ensemble model, Expert Systems with Applications 230: 120613.

[19] Majidpour, J., Rashid, T.A., Thinakaran, R., Batumalay, M., Dewi, D.A., Hassan, B.A., Dadgar, H. and Arabi, H. (2024). NSGA-II-DL: Metaheuristic optimal feature selection with deep learning framework for HER2 classification in breast cancer, IEEE Access 2(12): 38885-38898.

[20] Mengash, H.A., Alqahtani, H., Maray, M., Nour, M.K., Marzouk, R., Al-Hagery, M.A., Mohsen, H. and Al Duhayyim, M. (2023). Automated autism spectral disorder classification using optimal machine learning model, Computers, Materials and Continua 74(3): 5251-5265.

[21] Mirjalili, S. and Hashim, S.Z.M. (2012). BMOA: Binary magnetic optimization algorithm, International Journal of Machine Learning and Computing 2(3): 204-208.

[22] Mohammed, M.B., Salsabil, L., Shahriar, M., Tanaaz, S.S. and Fahmin, A. (2021). Identification of autism spectrum disorder through feature selection-based machine learning, 2021 24th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, pp. 1-6.

[23] Nogay, H.S. and Adeli, H. (2023). Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks, Biomedical Signal Processing and Control 79: 104234.

[24] Omuya, E.O., Okeyo, G.O. and Kimwele, M.W. (2021). Feature selection for classification using principal component analysis and information gain, Expert Systems with Applications 174: 114765.

[25] Parlett-Pelleriti, C.M., Stevens, E., Dixon, D. and Linstead, E.J. (2023). Applications of unsupervised machine learning in autism spectrum disorder research: A review, Review Journal of Autism and Developmental Disorders 10(3): 406-421.

[26] Paul, D., Jain, A., Saha, S. and Mathew, J. (2021). Multi-objective PSO based online feature selection for multi-label classification, Knowledge-Based Systems 222: 106966.

[27] Qasim, O.S., Al-Thanoon, N.A. and Algamal, Z.Y. (2020). Feature selection based on chaotic binary black hole algorithm for data classification, Chemometrics and Intelligent Laboratory Systems 204: 104104.

[28] Qi, X., Zhu, Y. and Zhang, H. (2017). A new meta-heuristic butterfly-inspired algorithm, Journal of Computational Science 23: 226-239.

[29] Rahman, M.M., Usman, O.L., Muniyandi, R.C., Sahran, S., Mohamed, S. and Razak, R.A. (2020). A review of machine learning methods of feature selection and classification for autism spectrum disorder, Brain Sciences 10(12): 949.

[30] Raj, S. and Masood, S. (2020). Analysis and detection of autism spectrum disorder using machine learning techniques, Procedia Computer Science 167: 994-1004.

[31] Sahu, S.K. and Verma, P. (2022). Classification of autistic spectrum disorder using deep neural network with particle swarm optimization, International Journal of Computer Vision and Image Processing 12(1): 1-11.

[32] Sayed, G.I., Hassanien, A.E. and Azar, A.T. (2019). Feature selection via a novel chaotic crow search algorithm, Neural Computing and Applications 31: 171-188.

[33] Shami, T.M., Mirjalili, S., Al-Eryani, Y., Daoudi, K., Izadi, S. and Abualigah, L. (2023). Velocity pausing particle swarm optimization: A novel variant for global optimization, Neural Computing and Applications 35(12): 9193-9223.

[34] Spencer, R., Thabtah, F., Abdelhamid, N. and Thompson, M. (2020). Exploring feature selection and classification methods for predicting heart disease, Digital Health 6, Article no. 205507620914777, DOI: 10.1177/2055207620914777.

[35] Thirumoorthy, K. and Muneeswaran, K. (2021). Feature selection using hybrid poor and rich optimization algorithm for text classification, Pattern Recognition Letters 147: 63-70.

[36] Uddin, M.J., Ahamad, M.M., Sarker, P.K., Aktar, S., Alotaibi, N., Alyami, S.A., Kabir, M.A. and Moni, M.A. (2023). An integrated statistical and clinically applicable machine learning framework for the detection of autism spectrum disorder, Computers 12(5): 92.

[37] Umamaheswari, K. and Parthiban, L. (2020). Improved whale optimization algorithm based feature selection with fuzzy rule base classifier for autism spectrum disorder diagnosis, International Journal of Advanced Research in Engineering and Technology 11(11): 41-55.

[38] Wang, H., Jing, X. and Niu, B. (2014). A weighted bacterial colony optimization for feature selection, Intelligent Computing in Bioinformatics: 10th International Conference, ICIC 2014, Taiyuan, China, pp. 379-389.

[39] Wang, H., Tan, L. and Niu, B. (2019). Feature selection for classification of microarray gene expression cancers using bacterial colony optimization with multi-dimensional population, Swarm and Evolutionary Computation 48: 172-181.

[40] Zhang, F., Wei, Y., Liu, J., Wang, Y., Xi, W. and Pan, Y. (2022). Identification of autism spectrum disorder based on a novel feature selection method and variational autoencoder, Computers in Biology and Medicine 148: 105854.

[41] Zhao, J., Zhang, D., He, Q. and Li, L. (2023). A hybrid-strategy-improved dragonfly algorithm for the parameter identification of an SDM, Sustainability 15(15): 11791.