Communications of the ACM - Special issue on parallelism
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
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
A probabilistic framework for memory-based reasoning
Artificial Intelligence
Inference for the Generalization Error
Machine Learning
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
A fast decision tree learning algorithm
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A Novel Bayes Model: Hidden Naive Bayes
IEEE Transactions on Knowledge and Data Engineering
Random one-dependence estimators
Pattern Recognition Letters
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Learning naive bayes for probability estimation by feature selection
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
The optimal distance measure for nearest neighbor classification
IEEE Transactions on Information Theory
An Augmented Value Difference Measure
Pattern Recognition Letters
Hi-index | 0.10 |
It is well known that the naive Bayesian classifier assumes the attribute independence given the class. According to our observation, some distance functions also assume the attribute independence, such as Value Difference Metric (VDM). Short and Fukunaga Metric (SFM) is another widely used distance function, which does not assume the attribute independence. In this paper, we investigate the attribute independence assumption in VDM, and propose a Modified Short and Fukunaga Metric (MSFM) based on the attribute independence assumption. We find that MSFM is surprisingly similar to VDM. In fact, based on some assumptions, our MSFM can be regarded as a logarithmic modification of VDM. That is to say, in some sense, a logarithmic modification of SFM is equivalent to a logarithmic modification of VDM. Our experimental results on a large number of UCI benchmark datasets show that MSFM significantly outperforms SFM and SF2LOG (another improved version of SFM), and almost ties VDM.