Juan Gabriel V. Mejia
ABSTRACT
Support vector machine (SVM) is a machine learning algorithm used for classification. SVM is commonly used in image, video, speech, and text recognition. The C value in SVM determines the degree of error for misclassification that the model will accept in the training set for the sake of generalization. One problem in the algorithm lies in finding the optimal value of this parameter. This problem affects the SVM model in achieving higher performance by chances of overfitting. In this paper, the Giza Pyramids Construction (GPC) Algorithm was proposed to find the optimal value of C. GPC finds an optimal C value by simulating how the workers were able to push stone blocks for the construction of the Giza Pyramid in the ancient times. The best position of the worker where it will be able to push the block most efficiently is calculated and was used as the C value for the SVM model. The proposed model was compared to the original SVM. Confusion matrix, F1-score, and accuracy were used to measure the performance of each model. The proposed model achieved a global accuracy of 97.27% while the original SVM achieved 94.54% in the Chars74K handwriting dataset. GPC-SVM has also achieved higher global accuracies on the MNIST dataset and the Chars74K natural images dataset compared to the original SVM. It was concluded that the proposed model achieved greater performance on all datasets compared to the original SVM in recognizing digits in the dataset.
- T. Xie, J. Yao, and Z. Zhou, “DA-based parameter optimization of combined kernel support vector machine for cancer diagnosis,” Processes, vol. 7, no. 5, May 2019, doi: 10.3390/pr7050263.Google Scholar
- S. Harifi, J. Mohammadzadeh, M. Khalilian, and S. Ebrahimnejad, “Giza Pyramids Construction: an ancient-inspired metaheuristic algorithm for optimization,” Evolutionary Intelligence, vol. 14, no. 4, pp. 1743–1761, Dec. 2021, doi: 10.1007/s12065-020-00451-3.Google Scholar
- E. Tuba, M. Tuba, and D. Simian, Handwritten Digit Recognition by Support Vector Machine Optimized by Bat Algorithm.Google Scholar
- A. Tharwat, A. E. Hassanien, and B. E. Elnaghi, “A BA-based algorithm for parameter optimization of Support Vector Machine,” Pattern Recognition Letters, vol. 93, pp. 13–22, Jul. 2017, doi: 10.1016/j.patrec.2016.10.007.Google ScholarCross Ref
- S. Wang, L. Dong, and H. Hua, “Parameter optimization of support vector machine based on improved grid algorithm,” in Journal of Physics: Conference Series, Dec. 2020, vol. 1693, no. 1. doi: 10.1088/1742-6596/1693/1/012108.Google ScholarCross Ref
- Bhavikkmuar, S. M. (2020, June 9). Advantages of Support Vector Machines (SVM). OpenGenus IQ: Computing Expertise & Legacy. https://iq.opengenus.org/advantages-of-svm/Google Scholar
- LeCun, Y., Cortes, C., & Burges, C. J. C. (1999). MNIST handwritten digit database [The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.].Google Scholar
- sentdex. (2016, May 31). Soft Margin SVM - Practical Machine Learning Tutorial with Python p.31 [Video]. YouTube. https://www.youtube.com/watch?v=JHaqodAQqiIGoogle Scholar
- Dato-on, D. (2018, May 19). MNIST in CSV (Version 2) [Dataset]. https://www.kaggle.com/oddrationale/mnist-in-csvGoogle Scholar
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20 (3), 273–297. https://doi.org/10.1007/bf00994018Google ScholarDigital Library
- Islam, M., Anh Dinh, Wahid, K., & Bhowmik, P. (2017). Detection of potato diseases using image segmentation and multiclass support vector machine.2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). https://doi.org/10.1109/ccece.2017.7946594Google Scholar
- Khemchandani, R., & Sharma, S. (2016). Robust least squares twin support vector machine for human activity recognition. Applied Soft Computing, 47, 33–46. https://doi.org/10.1016/j.asoc.2016.05.025Google ScholarDigital Library
- Aida-zade, K., Xocayev, A., & Rustamov, S. (2016). Speech recognition using Support Vector Machines.2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT). https://doi.org/10.1109/icaict.2016.7991664Google Scholar
- Katiyar, G. (2017). Off-Line Handwritten Character Recognition System Using Support Vector Machine. American Journal of Neural Networks and Applications, 3 (2), 22. https://doi.org/10.11648/j.ajnna.20170302.12Google ScholarCross Ref
- Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. Machine Learning, 101–121. https://doi.org/10.1016/b978-0-12-815739-8.00006-7Google Scholar
- Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295–316. https://doi.org/10.1016/j.neucom.2020.07.061Google ScholarCross Ref
- de Campos, T., & Varma, M. (2009). The Chars74K image dataset [Dataset]. http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/Google Scholar
- Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2017). Hyperband: A novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, 18 (1), 6765-6816.Google Scholar
- Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks. https://doi.org/10.1109/icnn.1995.4889684Google ScholarCross Ref
- Oliveira, T. (2021, October 2). Numerical Images [Dataset]. https://www.kaggle.com/pintowar/numerical-imagesGoogle Scholar
- Kröger, O. (2016, January 29). Tensorflow, MNIST and your own handwritten digits. Medium. https://medium.com/@o.kroeger/tensorflow-mnist-and-your-own-handwritten-digits-4d1cd32bbab4Google Scholar
- sentdex. (2018, August 18). Loading in your own data - Deep Learning basics with Python, TensorFlow and Keras p.2 [Video]. YouTube. https://www.youtube.com/watch?v=j-3vuBynnOE&t=30sGoogle Scholar
- Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9 (1), 62-66.Google Scholar
- Dey, R., Balabantaray, R. C., & Mohanty, S. (2021). Sliding window based off-line handwritten text recognition using edit distance. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-021-10988-9Google ScholarDigital Library
- OpenCV. (n.d.). OpenCV: Geometric Transformations of Images. https://docs.opencv.org/4.x/da/d6e/tutorial_py_geometric_transformations.html#gsc.tab=0Google Scholar
- Ansari, S. (2020). Building Computer Vision Applications Using Artificial Neural Networks: With Step-by-Step Examples in OpenCV and TensorFlow with Python (1st ed.). Apress.Google ScholarCross Ref
- McManus, I. C., Stöver, K., & Kim, D. (2011). Arnheim's Gestalt Theory of Visual Balance: Examining the Compositional Structure of Art Photographs and Abstract Images. I-Perception, 2 (6), 615–647. https://doi.org/10.1068/i0445aapGoogle ScholarCross Ref
Recommendations
Wavelet twin support vector machines based on glowworm swarm optimization
Twin support vector machine is a machine learning algorithm developing from standard support vector machine. The performance of twin support vector machine is always better than support vector machine on datasets that have cross regions. Recently ...
A twin-hypersphere support vector machine classifier and the fast learning algorithm
This paper formulates a twin-hypersphere support vector machine (THSVM) classifier for binary recognition. Similar to the twin support vector machine (TWSVM) classifier, this THSVM determines two hyperspheres by solving two related support vector ...
An overview on nonparallel hyperplane support vector machine algorithms
Support vector machine (SVM) has attracted substantial interest in the community of machine learning. As the extension of SVM, nonparallel hyperplane SVM (NHSVM) classification algorithms have become current researching hot spots in machine learning ...
Comments