An Efficient Random Decision Tree Algorithm for Case-Based Reasoning Systems

We present an efficient random decision tree algorithm for case-based reasoning systems. We combine this algorithm with a simple similarity measure based on domain knowledge to create a stronger hybrid algorithm. This combination is based on our general approach for combining lazy and eager learning methods. We evaluate the resulting algorithms on a case base of patient records in a palliative care domain. Our hybrid algorithm consistently produces a lower average error than the base algorithms.