Fast Training of Support Vector Machine for Forest Fire Prediction
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Support Vector Machine (SVM) is a binary classification model, which aims to find the optimal separating hyperplane with the maximum margin in order to classify the data. The maximum margin SVM is obtained by solving a convex Quadratic Programming Problem (QPP) and is termed as the hard-margin linear SVM. This optimization problem can solve using commercial Quadratic Programming (QP) code, i.e., Lagrange multipliers. However, the training process is time-consuming. Several decomposition methods have been proposed, which split the problem into a sequence of smaller sub-problems. The Sequential Minimal Optimization (SMO) algorithm is a widely utilized decomposition for SVM. In this paper, SMO algorithm for SVM for regression is utilized to forecast forest fires. Moreover, the Stochastic Gradient Descent (SGD) algorithm is employed for comparison purposes. The comparative results analysis shows that SVR-SMO model outperforms the SGDRegressor model in predicting forest fires.
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