The multi-class metric problem in nearest neighbour discrimination rules
Pattern Recognition
Instance-Based Learning Algorithms
Machine Learning
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
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
Machine Learning - Special issue on learning with probabilistic representations
A probabilistic framework for memory-based reasoning
Artificial Intelligence
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Inference for the Generalization Error
Machine Learning
Discriminative parameter learning for Bayesian networks
Proceedings of the 25th international conference on Machine learning
A fast decision tree learning algorithm
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
One Dependence Value Difference Metric
Knowledge-Based Systems
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A Modified Short and Fukunaga Metric based on the attribute independence assumption
Pattern Recognition Letters
The optimal distance measure for nearest neighbor classification
IEEE Transactions on Information Theory
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How to learn distances from categorical variables (nominal attributes) is a key problem in instance-based learning and other paradigms of machine learning. Recent work in distance learning has shown that a surprisingly simple Value Difference Metric (VDM), with strong assumptions of independence among attributes, is competitive with state-of-the-art distance functions such as Short and Fukunaga Metric (SFM) and Minimum Risk Metric (MRM). This fact raises the question of whether a distance function with less restrictive assumptions can perform even better. In order to answer this question, we proposed an augmented memory-based reasoning (MBR) transform. Based on our augmented MBR transform, we then developed an Augmented Value Difference Measure (AVDM) for learning distances from categorical variables. We experimentally tested our AVDM using 36 natural domains and three artificial Monk's domains, taken from the University of California at Irvine repository, and compared it to its competitor such as VDM, SFM, MRM, ODVDM, and MSFM. The compared results show that our AVDM can generally improve accuracy in domains that involve correlated attributes without reducing accuracy in ones that do not.