Comparison of classical machine learning algorithms in the task of handwritten digits classification
Oleksandr Voloshchenko
oleksandr.voloshchenko@pollub.edu.plStudent (Poland)
Małgorzata Plechawska-Wójcik
(Poland)
Abstract
The purpose of this paper is to compare classical machine learning algorithms for handwritten number classification. The following algorithms were chosen for comparison: Logistic Regression, SVM, Decision Tree, Random Forest and k-NN. MNIST handwritten digit database is used in the task of training and testing the above algorithms. The dataset consists of 70,000 images of numbers from 0 to 9. The algorithms are compared considering such criteria as the learning speed, prediction construction speed, host machine load, and classification accuracy. Each algorithm went through the training and testing phases 100 times, with the desired KPIs retained at each iteration. The results were averaged to reach reliable outcomes.
Keywords:
machine learning, classification, MNIST, classical algorithmsReferences
A.L. Samuel, Some studies in machine learning using the game of checkers, IBM Journal of Research and Development 44 (2000) 206-226.
DOI: https://doi.org/10.1147/rd.441.0206
Google Scholar
J.M. Peña-Barragán, P.A. Gutiérrez, C. Martínez, J. Six, R.E. Plant, F. López-Granados, Object-Based Image Classification of Summer Crops with Machine Learning Methods, Remote Sensing 6 (2014) 5019-5041.
DOI: https://doi.org/10.3390/rs6065019
Google Scholar
P. Mohapatra, B. Panda, S. Swain, Enhancing histopathological breast cancer image classification using deep learning, The International Journal of Innovative Technology and Exploring Engineering 8 (2019) 2024-2032.
Google Scholar
N.H. Aung, Y.K. Thu, S.S. Maung, Feature Based Myanmar Fingerspelling Image Classification Using SIFT, SURF and BRIEF, Proceedings of the 17th International Conference on Computer Applications (ICCA 2019) (2019) 245-253.
Google Scholar
I.H. Sarker, A.S. Kayes, P. Watters, Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage, Journal of Big Data 6 (2019) 1-28.
DOI: https://doi.org/10.1186/s40537-019-0219-y
Google Scholar
R. Razavi, A. Gharipour, M. Gharipour, Depression screening using mobile phone usage metadata: a machine learning approach, Journal of the American Medical Informatics Association 27 (2020) 522-530.
DOI: https://doi.org/10.1093/jamia/ocz221
Google Scholar
M. Pennacchiotti, A.-M. Popescu, A Machine Learning Approach to Twitter User Classification, Proceedings of the International AAAI Conference on Web and Social Media 5 (2021) 281-288.
DOI: https://doi.org/10.1609/icwsm.v5i1.14139
Google Scholar
Y. Nieto, V. Gacía-Díaz, C. Montenegro, C.C. González, R.G. Crespo, Usage of machine learning for strategic decision making at higher educational institutions, IEEE Access 7 (2019) 75007-75017.
DOI: https://doi.org/10.1109/ACCESS.2019.2919343
Google Scholar
L. Bottou, C. Cortes, J.S. Denker, H. Drucker, I. Guyon, L.D. Jackel, Y. LeCun, U.A. Muller, E. Sackinger, P. Simard, V. Vapnik, Comparison of classifier methods: a case study in handwritten digit recognition, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3-Conference C: Signal Processing (Cat. No. 94CH3440-5) 2 (1994) 77-82.
Google Scholar
Y. LeCun, L.D. Jackel, L. Bottou, A. Brunot, C.Cortes, J. Denker, H. Drucker, I. Guyon, U.A. Muller, E. Sackinger, P. Simard, Comparison of learning algorithms for handwritten digit recognition, International conference on artificial neural networks 60 (1995) 53-60.
Google Scholar
B. El Kessab, C. Daoui, B. Bouikhalene, R. Salouan, A Comparative Study between the Support Vectors Machines and the K-Nearest Neighbors in the Handwritten Latin Numerals Recognition, International Journal of Signal Processing, Image Processing and Pattern Recognition 8 (2015) 325-336.
DOI: https://doi.org/10.14257/ijsip.2015.8.2.31
Google Scholar
K. Zhao, Handwritten digit recognition and classification using machine learning, M.Sc. in Computing (Data Analytics), Technological University Dublin (2018).
Google Scholar
C. Kaensar, A comparative study on handwriting digit recognition classifier using neural network, support vector machine and k-nearest neighbor, The 9th International Conference on Computing and InformationTechnology (IC2IT2013) (2013) 155-163.
DOI: https://doi.org/10.1007/978-3-642-37371-8_19
Google Scholar
N.A. Hamid, N.N. Sjarif, Handwritten recognition using SVM, KNN and neural network, arXiv preprint arXiv:1702.00723 (2017).
Google Scholar
T.A. Assegie, P.S. Nair, Handwritten digits recognition with decision tree classification: a machine learning approach, International Journal of Electrical and Computer Engineering (IJECE) 9 (2019) 4446-4451.
DOI: https://doi.org/10.11591/ijece.v9i5.pp4446-4451
Google Scholar
L. Breiman, Random forests, UC Berkeley TR567 (1999).
Google Scholar
Authors
Małgorzata Plechawska-WójcikPoland
Statistics
Abstract views: 307PDF downloads: 209
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.