A lazy algorithm for decision tree induction based on importance of attributes

This paper applies lazy idea to fuzzy decision tree induction. A new algorithm is proposed in this paper based on important of attributes. This algorithm, which does not generate a decision tree for all training examples, only determines a specified path for each test case. Obviously the algorithm has reduced the computational effort of training but increase the complexity of testing. We experimentally find that the proposed algorithm is superior to the traditional one on robustness.