Support Vector Machine optimization with fractional gradient descent for data classification

Dian Puspita Hapsari, Imam Utoyo, Santi Wulan Purnami

Abstract


Data classification has several problems one of which is a large amount of data that will reduce computing time. SVM is a reliable linear classifier for linear or non-linear data, for large-scale data, there are computational time constraints. The Fractional gradient descent method is an unconstrained optimization algorithm to train classifiers with support vector machines that have convex problems. Compared to the classic integer-order model, a model built with fractional calculus has a significant advantage to accelerate computing time. In this research, it is to conduct investigate the current state of this new optimization method fractional derivatives that can be implemented in the classifier algorithm. The results of the SVM Classifier with fractional gradient descent optimization, it reaches a convergence point of approximately 50 iterations smaller than SVM-SGD. The process of updating or fixing the model is smaller in fractional because the multiplier value is less than 1 or in the form of fractions. The SVM-Fractional SGD algorithm is proven to be an effective method for rainfall forecast decisions.

Full Text:

PDF

References


A. J. Gallego, J. Calvo-Zaragoza, J. J. Valero-Mas, and J. R. Rico-Juan, “Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation,” Pattern Recognit., 2018.

S. Al-Saqqa, G. Al-Naymat, and A. Awajan, “A large-scale sentiment data classification for online reviews under apache spark,” in Procedia Computer Science, 2018.

X. Gu, F. lai Chung, and S. Wang, “Fast convex-hull vector machine for training on large-scale ncRNA data classification tasks,” Knowledge-Based Syst., 2018.

H. Liu, S. Yang, S. Gou, P. Chen, Y. Wang, and L. Jiao, “Fast Classification for Large Polarimetric SAR Data Based on Refined Spatial-Anchor Graph,” IEEE Geosci. Remote Sens. Lett., 2017.

L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proceedings of COMPSTAT 2010 - 19th International Conference on Computational Statistics, Keynote, Invited and Contributed Papers, 2010.

Y. F. Pu et al., “Fractional partial differential equation denoising models for texture image,” Sci. China Inf. Sci., 2014.

D. Chen, Y. Q. Chen, and D. Xue, “Fractional-order total variation image denoising based on proximity algorithm,” Appl. Math. Comput., 2015.

Z. A. Khan, N. I. Chaudhary, and S. Zubair, “Fractional stochastic gradient descent for recommender systems,” Electron. Mark., 2019.

D. Chen, H. Sheng, Y. Q. Chen, and D. Xue, “Fractional-order variational optical flowmodel for motion estimation,” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 2013.

C. J. Zuñiga Aguilar, J. F. Gómez-Aguilar, V. M. Alvarado-Martínez, and H. M. Romero-Ugalde, “Fractional order neural networks for system identification,” Chaos, Solitons and Fractals, 2020.

H. Wang, Y. Yu, G. Wen, S. Zhang, and J. Yu, “Global stability analysis of fractional-order Hopfield neural networks with time delay,” Neurocomputing, 2015.

M. U. Akhmet and M. Karacaören, “A Hopfield neural network with multi-compartmental activation,” Neural Comput. Appl., 2018.

H. P. Hu, J. K. Wang, and F. L. Xie, “Dynamics analysis of a new fractional-order hopfield neural network with delay and its generalized projective synchronization,” Entropy, 2019.

M. Caputo and M. Fabrizio, “A new definition of fractional derivative without singular kernel,” Prog. Fract. Differ. Appl., 2015.

A. I. Journal, M. Caputo, and M. Fabrizio, “Progress in Fractional Differentiation and Applications A new Definition of Fractional Derivative without Singular Kernel,” Progr. Fract. Differ. Appl, 2015.

Y. Chen, Y. Wei, Y. Wang, and Y. Q. Chen, “Fractional order gradient methods for a general class of convex functions,” in Proceedings of the American Control Conference, 2018.

Y. Wei, Y. Chen, S. Cheng, and Y. Wang, “Discussion on fractional order derivatives,” IFAC-PapersOnLine, 2017.

J. Losada and J. J. Nieto, “Properties of a new fractional derivative without singular kernel,” Prog. Fract. Differ. Appl., 2015.

C. Bao, Y. Pu, and Y. Zhang, “Fractional-Order Deep Backpropagation Neural Network,” Comput. Intell. Neurosci., 2018.

S. Khan, J. Ahmad, I. Naseem, and M. Moinuddin, “A Novel Fractional Gradient-Based Learning Algorithm for Recurrent Neural Networks,” Circuits, Syst. Signal Process., 2018.




DOI: https://doi.org/10.31284/j.jasmet.2021.v2i1.1467

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Dian Puspita Hapsari, Imam Utoyo, Santi Wulan Purnami

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Mailing Address: Journal of Applied Sciences, Management and Engineering Technology - ITATS Institut Teknologi Adhi Tama Surabaya Jl. Arief Rahman Hakim No.100, Surabaya 60117 email: [email protected] Website : https://ejurnal.itats.ac.id/jasmet/index