Side effect of cut in decision tree generation for continuous attributes

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 (V, +) to overcome this problem. Experimental results show that this method can obtain good performance.