We introduce a novel algorithm for decision tree learning in the multi-instance setting as originally defined by Dietterich et al. It differs from existing multi-instance tree lea...
This paper extends previous work on skewing, an approach to problematic functions in decision tree induction. The previous algorithms were applicable only to functions of binary v...
This paper presents a novel hybrid method combining genetic programming and decision tree learning. The method starts by estimating a benchmark level of reasonable accuracy, based ...
We investigate an inherent limitation of top-down decision tree induction in which the continuous partitioning of the instance space progressively lessens the statistical support o...
Decision trees have proved to be valuable tools for the description, classi cation and generalizationof data. Work on constructingdecisiontrees from data exists in multiplediscipli...