A new decision tree learning algorithm called IDX is described. More general than existing algorithms, IDX addresses issues of decision tree quality largely overlooked in the arti...
This paper explores the problem of how to construct lazy decision tree ensembles. We present and empirically evaluate a relevancebased boosting-style algorithm that builds a lazy ...
Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are wort...
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...
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...