Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Meta Analysis of Classification Algorithms for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Vector Quantization Technique for Nonparametric Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Nonlinearity of Pattern Classifiers
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
On Classifier Domains of Competence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-category classification by kernel based nonlinear subspace method
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data characterization for effective prototype selection
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Balancing strategies and class overlapping
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On Using Prototype Reduction Schemes to Optimize Kernel-Based Fisher Discriminant Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
An algorithm for a selective nearest neighbor decision rule (Corresp.)
IEEE Transactions on Information Theory
Information Sciences: an International Journal
Analysis of data complexity measures for classification
Expert Systems with Applications: An International Journal
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In most pattern recognition (PR) applications, it is advantageous if the accuracy (or error rate) of the classifier can be evaluated or bounded prior to testing it in a real-life setting. It is also well known that if the two class-conditional distributions have a large overlapping volume (almost all the available work on ''overlapping of classes'' deals with the case when there are only two classes), the classification accuracy is poor. This is because if we intend to use the classification accuracy as a criterion for evaluating a PR system, the points within the overlapping volume tend to lead to maximal misclassification. Unfortunately, the computation of the indices which quantify the overlapping volume is expensive. In this vein, we propose a strategy of using a prototype reduction scheme (PRS) to approximately, but quickly, compute the latter. In this paper, we demonstrate, first of all, that this is an extremely expedient proposition. Indeed, we show that by completely discarding (we are not aware of any reported scheme which discards ''irrelevant'' sample (training) points, and which simultaneously attains to an almost-comparable accuracy) the points not included by the PRS, we can obtain a reduced set of sample points, using which, in turn, the measures for the overlapping volume can be computed. The value of the corresponding figures is comparable to those obtained with the original training set (i.e., the one which considers all the data points) even though the computations required to obtain the prototypes and the corresponding measures are significantly less. The proposed method has been rigorously tested on artificial and real-life datasets, and the results obtained are, in our opinion, quite impressive-sometimes faster by two orders of magnitude.