Communications of the ACM - Special issue on parallelism
The multi-class metric problem in nearest neighbour discrimination rules
Pattern Recognition
Trading MIPS and memory for knowledge engineering
Communications of the ACM
Artificial Intelligence Review - Special issue on lazy learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Data Compression and Local Metrics for Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Interactive Case-Based Planning for Forest Fire Management
Applied Intelligence
Improving Minority Class Prediction Using Case-Specific Feature Weights
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Retrieval in a Prototype-Based Case Library: A Case Study in Diabetes Therapy Revision
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Learning a Local Similarity Metric for Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Probability Based Metrics for Locally Weighted Naive Bayes
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Learning similarity measures: a formal view based on a generalized CBR model
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Organizing large case library by linear programming
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Specific-class distance measures for nominal attributes
AI Communications
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This paper is focused on a class of metrics for the Nearest Neighbor classifier, whose definition is based on statistics computed on the case base. We show that these metrics basically rely on a probability estimation phase. In particular, we reconsider a metric proposed in the 80's by Short and Fukunaga, we extend its definition to an input space that includes categorical features and we evaluate empirically its performance. Moreover, we present an original probability based metric, called Minimum Risk Metric (MRM), i.e. a metric for classification tasks that exploits estimates of the posterior probabilities. MRM is optimal, in the sense that it optimizes the finite misclassification risk, whereas the Short and Fukunaga Metric minimize the difference between finite risk and asymptotic risk. An experimental comparison of MRM with the Short and Fukunaga Metric, the Value Difference Metric, and Euclidean-Hamming metrics on benchmark datasets shows that MRM outperforms the other metrics. MRM performs comparably to the Bayes Classifier based on the same probability estimates. The results suggest that MRM can be useful in case-based applications where the retrieval of a nearest neighbor is required.