A fast cutpoints sieve method for interval-valued decision tree

In this paper, the concept of the frequently covered points (FCP) and the infrequently covered points (ICP) is presented. By means of the cutpoints sieve method, we can rapidly pick out the corresponding cutpoints of ICP, namely preferred cutpoints, from all pending cutpoints of interval attributes. And then, only preferred cutpoints are used for computing information entropy of partition (IEP). Finally, the interval-valued decision tree can be built by IEP. The experiment indicates that, in general, this method could, to a great extent, reduce the computational complexity of creation of decision tree, thereby, improving the efficiency of classification.