An Optimized Algorithm of Decision Tree Based on Rough Sets Model

An optimized decision tree algorithm based on rough sets model is proposed. Firstly the most popular decision tree algorithms, which are based rough sets model, usually partition examples too detailed to avoid the negative impact caused by a few special examples on decision tree because of the classification accuracy. The inhibitory factors are put forward in the forming process of the algorithm to cut branches for decision tree, avoiding redundant steps of cutting branches later. Secondly the condition attribute and decision attribute are matched in every division to avoid unnecessary calculation and improve the efficiency of the algorithm.