A Hill-Climbing Approach to Construct Near-Optimal Decision Trees
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:513-519, 1995.
We consider the problem of identifying the state of an $n$ component coherent system, where each component can be working or failed. It is costly to determine the states of the components. The goal is to find a decision tree which specifies the order of the components to be tested with minimum expected cost. The problem is known to be NP-hard. We present an extremely promising heuristic method for creating effective decision trees, and computational results show that the method obtains optimal solutions for $95 %$ of the cases tested.