C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
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
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Classification of microarrays to nearest centroids
Bioinformatics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Generating estimates of classification confidence for a case-based spam filter
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Situated Cognition in the Semantic Web Era
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
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In many recognition tasks, a simple discrete class label is not sufficient and ranking of the classes is desirable; in others, a numeric score that represents the confidence of class membership for multiple classes is also required. Differential diagnosis in medical domains and terrain classification in surveying are prime examples. The Minimum Distance Classifier is a well-known, simple and efficient scheme for producing multi-class probabilities. However, when features contribute unequally to the classification, noisy and irrelevant features can distort the distance function. We enhance the minimum distance classifier with feature weights leading to the Feature Weighted Minimum Distance classifier. We empirically compare minimum distance classifier and its enhanced feature weighted version with a number of standard classifiers. We also present preliminary results on medical images with acceptable performance and better interpretability.