SVM-SMO-SGD: A hybrid-parallel support vector machine algorithm using sequential minimal optimization with stochastic gradient descent

The Support Vector Machine (SVM) method is one of the popular machine learning algorithms as it gives high accuracy. However, like most machine learning algorithms, the resource consumption of the SVM algorithm in terms of time and memory increases linearly as the dataset grows. In this study, a parallel-hybrid algorithm that combines SVM, Sequential Minimal Optimization (SMO) with Stochastic Gradient Descent (SGD) methods have been proposed to optimize the calculation of the weight costs. The performance of the proposed SVM-SMO-SGD algorithm was compared with classical SMO and Compute Unified Device Architecture (CUDA) based approaches on the well-known datasets (i.e., Diabetes, Healthcare Stroke Prediction, Adults) with 520, 5110, and 32,560 samples, respectively. According to the results, Sequential SVM-SMO-SGD is 3.81 times faster in terms of time, and 1.04 times more efficient RAM consumption than the classical SMO algorithm. The parallel SVM-SMO-SGD algorithm, on the other hand, is 75.47 times faster than the classical SMO algorithm in terms of time. It is also 1.9 times more efficient in RAM consumption. The overall classification accuracy of all algorithms is 87% in the Diabetes dataset, 95% in the Healthcare Stroke Prediction dataset, and 82% in the Adults dataset.

Automated summary

A parallel-hybrid algorithm combining SVM, SMO, and SGD methods has been proposed to optimize the computation of weight costs in the SVM algorithm. Compared with traditional methods, the proposed algorithm is significantly faster and more efficient in terms of time and memory consumption.