Concurrent discretization of multiple attributes

Abstract

Better decision trees can be learnt by merging continuous values into intervals. Merging of values, however, could introduce inconsistencies to the data, or information loss. When it is desired to maintain a certain consistency, interval mergings in one attribute could disable those in another attribute. This interaction raises the issue of determining the order of mergings. We consider a globally greedy heuristic that selects the “best” merging from all continuous attributes at each step. We present an implementation of the heuristic in which the best merging is determined in a time independent of the number of possible mergings. Experiments show that intervals produced by the heuristic lead to improved decision trees.