Speed Optimizations to Parzen Window Classifier Using Probability Approximation, Discretization and Compression

Year 2024, Volume: 16 Issue: 2, 507 - 517, 31.12.2024

İbrahim Cem Baykal

https://doi.org/10.47000/tjmcs.1333685

Abstract

Parzen window estimators can model any type of complicated probability density manifolds. However, when it comes to real life applications, they are not as popular as the Artificial Neural Networks or the Support Vector Machines. That is mainly because Parzen window classifiers require long and complex calculations during the classification process. This article introduces speed optimization methods for Parzen window classifier that makes this classifier faster than any other convergent classifier at a small performance cost. The method includes, discretization, look-up tables, approximation, and probabilistic compression. Experiments conducted on both computer generated and real-life data prove that the resultant classifier is only slightly less accurate than Artificial Neural Networks and Support Vector Machines while immensely faster.

Keywords

probability density estimation, machine learning, speed optimization, Parzen window classifier.

Project Number

yok

References

• Guney, S., Kilinc, I., Hameed, A.A., Jamil, A., Abalone age prediction using machine learning, Mediterranean Conference on Pattern Recognition and Artificial Intelligence, Springer International Publishing, (2021), 879–883.
• https://en.wikipedia.org/wiki/Kernel (statistics)
• https://www.kaggle.com
• Jain, V., Singh, R., Gupta, A., Exploring binary classification models for Parkinson’s disease detection, Procedia Computer Science 235(2024), 2332–2341.
• Kaur, S., Chaudhary, S., Thakur, A., Bajaj, R., Gupta, A. et al. Abalone age prediction using optimized ensembel model, IEEE 11th International Conference on System Modeling & Advancement in Research Trends, (2022), 1023–1027.
• Kawamura, T., Roberts, R.D., Takami, H., Importance of periphyton in Abalone culture, Periphyton: Ecology, Exploitation and Management, (2005), 269–883.
• Kecman, V., Huang, T.M., Vogt., M., Iterative single data algorithm for training kernel machines from huge data sets: Theory and Performance, In Support Vector Machines: Theory and Applications. Edited by Lipo Wang, 255–274. Berlin, Springer-Verlag, 2005.
• Levenberg, K., A method for the solution of certain non-linear problems in least squares, Quarterly of Applied Mathematics, 2(2)(1944), 164–168.
• Marquardt, D., An algorithm for least-squares estimation of nonlinear parameters, SIAM Journal on Applied Mathematics, 11(2)(1963), 431–441.
• Misman, M.F., Samah, A.A., Ab Aziz, N.A., Majid, H.A., Shah, Z.A. et al. Prediction of abalone age using regression-based neural network, IEEE 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS), (2019), 23–28.
• Sahin, E., Saul, C.J., Ozsarfati, E., Yilmaz, A., Abalone life phase classification with deep learning, IEEE 5th International Conference on Soft Computing & Machine Intelligence, (ISCMI), (2018), 163–167.
• Shaikh, M.S., Alftieh, A.M., Evaluation of four classification algorithms for P300 based brain computer interface, Life Science Journal, 10(3)(2013), 879–883.
• Wang, X., Tiiio, P., Fardal, M.A., Raychaudhury, S., Babul, A., Fast parzen window density estimator, IEEE International Joint Conference on Neural Networks, 60(2009), 3267–3274.