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
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
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
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 volume1, 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 have less significance in determining the prototypes. 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 compute the latter. In this paper, we show that by completely discarding2 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 data sets, and the results obtained are, in our opinion, quite impressive - sometimes faster by two orders of magnitude.