Communications of the ACM - Special issue on parallelism
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Self-Organizing Maps
Probability Based Metrics for Nearest Neighbor Classification and Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Radial Basis Functions
Nearest Neighbors by Neighborhood Counting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
A flexible and robust similarity measure based on contextual probability
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Neighborhood Counting Measure and Minimum Risk Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least squares quantization in PCM
IEEE Transactions on Information Theory
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The classification accuracy of many machine learning methods depends upon their ability to accurately measure the similarity between different instances. Similarity is measured using a distance metric or measure. In this work, several novel distance measures for nominal values are proposed. These distance measures exploit the class of a training example against which a new instance is compared. The experiments, conducted using 50 benchmark data sets, indicate that the proposed functions are superior in many cases to the Value Difference Metric VDM that is widely used in instance based learning. Some of the proposed measures have proven to be less sensitive to missing values and noise in the training data sets and have maintained good classification accuracy in the presence of unknown and noisy attribute values. Like VDM, the proposed measures work only with labelled training data sets which makes them unsuitable for unsupervised learning methods.