When similar problems don't have similar solutions

The performance of a Case-Based Reasoning system relies on the integrity of its case base but in real life applications the available data used to construct the case base invariably contains erroneous, noisy cases. Automated removal of these noisy cases can improve system accuracy. In addition, error rates for nearest neighbour classifiers can often be reduced by removing cases to give smoother decision boundaries between classes. In this paper we argue that the optimal level of boundary smoothing is domain dependent and, therefore, our approach to error reduction reacts to the characteristics of the domain to set an appropriate level of smoothing. We present a novel, yet transparent algorithm, Threshold Error Reduction, which identifies and removes noisy and boundary cases with the aid of a local complexity measure. Evaluation results confirm it to be superior to benchmark algorithms.