Decision trees using local support vector regression models for large datasets

摘要

ABSTRACT Our proposed decision trees using local support vector regression models ( t SVR, rt SVR) aim to efficiently handle the regression task for large datasets. The learning algorithm t SVR of regression models is done by two main steps. The first one is to construct a decision tree regressor for partitioning the full training dataset into k terminal-nodes (subsets), followed which the second one is to learn the SVR model from each terminal-node to predict the data locally in a parallel way on multi-core computers. The algorithm rt SVR learns the random forest of decision trees with local SVR models for improving the prediction correctness against the t SVR model alone. The performance analysis shows that our algorithms t SVR, rt SVR are efficient in terms of the algorithmic complexity and the generalization ability compared to the classical SVR. The experimental results on five large datasets from UCI repository showed that proposed t SVR and rt SVR algorithms are faster than the standard SVR in training the non-linear regression model from large datasets while achieving the high correctness in the prediction. Typically, the average training time of t SVR and rt SVR are 1282.66 and 482.29 times faster than the standard SVR; Furthermore, t SVR and rt SVR improve 59.43%, 63.70% of the relative prediction correctness compared to the standard SVR.