Coal Quantity Prediction Using Least Squares Support Vector Machines

In this example, we implement a Least Squares Support Vector Machine (LS-SVM) approach for predicting coal quantity. LS-SVM is a machine learning algorithm that performs both classification and regression analysis. For regression tasks, we utilize its capability to predict continuous variable values, such as coal production quantities. The implementation typically involves several key steps: First, we collect relevant datasets containing features like coal production volumes, market prices, distribution channels, and geographical factors. These features serve as input variables for our predictive model. The LS-SVM algorithm then maps these inputs to a high-dimensional feature space using kernel functions (commonly radial basis function kernels) where linear regression is performed. Key implementation aspects include: - Data preprocessing and normalization to ensure feature scaling - Kernel parameter selection and regularization optimization - Solving linear systems using efficient matrix operations instead of quadratic programming - Model validation through cross-validation techniques This method enhances prediction accuracy compared to traditional regression approaches and supports better decision-making in the coal industry by providing reliable quantitative forecasts. The LS-SVM formulation converts inequality constraints into equality constraints, making it computationally efficient while maintaining good generalization performance.