Communications of the ACM - Special issue on parallelism
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
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning
A Monotonic Measure for Optimal Feature Selection
ECML '98 Proceedings of the 10th European Conference on Machine Learning
An Improved Learning Algorithm for Augmented Naive Bayes
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Inference for the Generalization Error
Machine Learning
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Structure identification of Bayesian classifiers based on GMDH
Knowledge-Based Systems
A Novel Bayes Model: Hidden Naive Bayes
IEEE Transactions on Knowledge and Data Engineering
On the classification performance of TAN and general Bayesian networks
Knowledge-Based Systems
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Learning a locality discriminating projection for classification
Knowledge-Based Systems
Data mining for exploring hidden patterns between KM and its performance
Knowledge-Based Systems
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
Dynamic k-nearest-neighbor naive bayes with attribute weighted
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
One dependence augmented naive bayes
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Learning tree augmented naive bayes for ranking
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Lazy averaged one-dependence estimators
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
RESYGEN: A Recommendation System Generator using domain-based heuristics
Expert Systems with Applications: An International Journal
An Augmented Value Difference Measure
Pattern Recognition Letters
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Many distance-related algorithms depend upon a good distance metric to be successful. The Value Difference Metric, simply VDM, is proposed to find reasonable distance metric between each pair of instances with nominal attribute values only. In VDM, all of the attributes are assumed to be fully independent, and the difference between two values of an attribute is only considered to be closer if they have more similar correlation with the output classes. It is obvious that the attribute independence assumption in VDM is rarely true in reality, which would harm its performance in the applications with complex attribute dependencies. In this paper, we single out an improved Value Difference Metric by relaxing its unrealistic attribute independence assumption. We call it One Dependence Value Difference Metric, simply ODVDM. In ODVDM, the structure learning algorithms for Bayesian network classifiers, such as tree augmented naive Bayes, are used to find the dependence relationships among the attributes. Our experimental results validate its effectiveness in terms of classification accuracy.