Side effect of cut in decision tree generation for continuous attributes
Xizhao Wang, Xiang-Hui Gao, Qiang He · 2010
There is a phenomenon that binary decision trees generated for continuous attributes have lower prediction accuracy on near boundary examples than total testing dataset. In this paper, we propose a new approach by fuzzifying crisp rules into fuzzy IF-THEN rules and using fuzzy matching operator (∧, +) to overcome this problem. Experimental results show that this method can obtain good performance.