A comparative study of algorithms for handwritten digit recognition. Case: MNIST
DOI:
https://doi.org/10.32870/recibe.v12i2.306Keywords:
MNIST, neural networks, pattern recognition, classification metrics, classification algorithmsAbstract
The present work presents a comparison of classification algorithms for the recognition of handwritten characters corresponding to the digits from zero to nine, using the MNIST database as a source of information. Exhaustive search techniques for hyperparameters from the KNN machine learning algorithms, logistic regression, Nayve Bayes, support vector machine, decision tree and random forest will be used. The result of the previous algorithms will be compared with a multilayer neural network and another convolutional network also designed in the work, the classification metrics accuracy, precision, recall and F1-score will be used for this purpose.References
Belete, D. M., & Huchaiah, M. D. (2022). Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results. International Journal of Computers and Applications, 44(9), 875-886.
Braga-Neto, U. (2020). Fundamentals of pattern recognition and machine learning. Springer.
Cho, G., Yim, J., Choi, Y., Ko, J., & Lee, S.-H. (2019). Review of machine learning algorithms for diagnosing mental illness. Psychiatry investigation, 16(4), 262.
Dhillon, A., & Verma, G. K. (2020). Convolutional neural network: A review of models, methodologies and applications to object detection. Progress in Artificial Intelligence, 9(2), 85-112.
Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences. Atmospheric Environment, 32(14), 2627-2636. https://doi.org/10.1016/S1352-2310(97)00447-0
Gasparetto, A., Marcuzzo, M., Zangari, A., & Albarelli, A. (2022). A survey on text classification algorithms: From text to predictions. Information, 13(2), 83.
Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for multi-class classification: An overview. arXiv preprint arXiv:2008.05756.
Griffis, J. C., Allendorfer, J. B., & Szaflarski, J. P. (2016). Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans. Journal of neuroscience methods, 257, 97-108.
Gutiérrez Esparza, G. O., Margain Fuentes, M. de L., Ramírez del Real, T. A., & Canul Reich, J. (2017). Un modelo basado en el Clasificador Naïve Bayes para la evaluación del desempeño docente. RIED. Revista iberoamericana de educación a distancia.
Hernández, A. Z., Rosales, G. A. G., Santiago, H. J. J., & Lee, M. M. (2022). Métricas de rendimiento para evaluar el aprendizaje automático en la clasificación de imágenes petroleras utilizando redes neuronales convolucionales. Ciencia Latina Revista Científica Multidisciplinar, 6(5), 4624-4637.
Kalmegh, S. R., & Padar, B. R. (2023). Empirical Study on Evaluation Metrics for Classification Algorithms. International Journal of Advanced Research in Science, Communication and Technology.
Kirişci, M. (2019). Comparison of artificial neural network and logistic regression model for factors affecting birth weight. SN Applied Sciences, 1(4), 378.
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2021). A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE transactions on neural networks and learning systems.
Llumiquinga Almeida, E. P. (2022). Estudio comparativo de los algoritmos de clasificación supervisada empleando datos artificiales. [B.S. thesis]. Quito, 2022.
Luu, S. T., Nguyen, H. P., Van Nguyen, K., & Nguyen, N. L.-T. (2020). Comparison between traditional machine learning models and neural network models for vietnamese hate speech detection. 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), 1-6.
Martínez-Toro, G. M., Rico-Bautista, D., & Romero-Riaño, E. (2019). Análisis comparativo de predicción dentro de bases de datos de cáncer: Una aplicación de aprendizaje automático. Revista Ibérica de Sistemas e Tecnologias de Informação, E17, 113-122.
Naranjo, L. T. L. (2022). Diseño de una interfaz cerebro computador (BCI) para la interacción con un sistema de rehabilitación de miembro superior.
Ray, S. (2019). A quick review of machine learning algorithms. 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon), 35-39.
Ruiz-Shulcloper, J., Guzmán Arenas, A., & Martínez-Trinidad, J. F. (1999). Enfoque lógico combinatorio al reconocimiento de patrones. I. Selección de Variables y Clasificación Supervisada, IPN, México.
Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., & Homayouni, S. (2020). Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308-6325.
Tanveer, M., Rajani, T., Rastogi, R., Shao, Y.-H., & Ganaie, M. A. (2022). Comprehensive review on twin support vector machines. Annals of Operations Research, 1-46.
Yann, L. (1998). The mnist database of handwritten digits. R.
Zhang, X.-Y., Liu, C.-L., & Suen, C. Y. (2020). Towards robust pattern recognition: A review. Proceedings of the IEEE, 108(6), 894-922.
