Instance-Based Learning Algorithms
Machine Learning
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to artificial neural systems
Introduction to artificial neural systems
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
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Case selection is a useful approach for increasing the efficiency and performance of case-based classifiers. Multiple techniques have been designed to perform case selection. This paper empirically investigates how class imbalance in the available set of training cases can impact the performance of the resulting classifier as well as properties of the selected set. In this study, the experiments are performed using a dataset for the problem of detecting breast masses in screening mammograms. The classification problem was binary and we used a k-nearest neighbor classifier. The classifier's performance was evaluated using the Receiver Operating Characteristic (ROC) area under the curve (AUC) measure. The experimental results indicate that although class imbalance reduces the performance of the derived classifier and the effectiveness of selection at improving overall classifier performance, case selection can still be beneficial, regardless of the level of class imbalance.