On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
k-nearest Neighbor Classification on Spatial Data Streams Using P-trees
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
A generalization of dissimilarity representations using feature lines and feature planes
Pattern Recognition Letters
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This paper presents an experimental comparison of the nearest feature classifiers, using an approach based on binomial tests in order to evaluate their strengths and weaknesses. In addition, classification accuracies and the accuracy-dimensionality tradeoff have been considered as comparison criteria. We extend two of the nearest feature classifiers to label the query point by a majority vote of the samples. Comparisons were carried out for face recognition using ORL database. We apply the eigenface representation for feature extraction. Experimental results showed that even though the classification accuracy of k-NFP outperforms k-NFL in some dimensions, these rate differences do not have statistical significance.