Optimization Algorithm for Multivariate Decision Trees Based on VPRS
基于VPRS多变量决策树优化算法

When construct multivariate decision trees, noise data reduced the training efficiency and quality of model, most of the present pruning methods aimed at leaf node to eliminate the influence of noise data, but not pay attention to the disturbed problem of noise data when selected testing attribute. In order to solve the problem, extends the relative core of attributes in rough sets theory to variable precision rough set(VPRS), and uses it for selection of initial variables for decision tree; extends the concept of generalization of one equivalence relation with respect to another one, to relative generalization equivalence relation under mostly-contained condition, and uses it for decision tree initial attribute check;propose an algorithm for multivariate decision tree that can avoid disturbance of noisy data. Finally, validated the algorithm by an experiment.