Optimization of Decision Tree Based on Variable Precision Rough Set

This paper analyzes the existing decision tree classification algorithms based on variable precision rough set and finds that these algorithms have better classification accuracies and can tolerate the noise data. But when choosing the best attribute using variable precision rough set, these algorithms still have the shortages in ID3. That is, these algorithms also tend to choose the attribute with more attribute values as the splitting node, but this attribute is often not the best attribute. Therefore, this paper proposes a new attribute selection criterion. When the algorithm selects a new attribute, not only the number of attribute values in the current node, but also the size of variable precision explicit region in the lower node is taken into consideration. In other words, the size of variable precision explicit region of attributes in two levels of the decision tree is used. With the new approach to select attributes, the proposed algorithm overcomes the lack of ID3 algorithm and also has the advantages of variable precision rough set. Comparison between the present method and the existing methods based on variable precision rough set shows that the improved algorithm (MVPRSDT) has better classification performance.