Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Self-organizing maps
Experiments with a featureless approach to pattern recognition
Pattern Recognition Letters - special issue on pattern recognition in practice V
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relational discriminant analysis
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
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
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-prototype classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
On Using Dimensionality Reduction Schemes to Optimize Dissimilarity-Based Classifiers
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A Dynamic Programming Technique for Optimizing Dissimilarity-Based Classifiers
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Forensic Authorship Attribution Using Compression Distances to Prototypes
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
An empirical comparison of Kernel-based and dissimilarity-based feature spaces
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
A multiple combining method for optimizing dissimilarity-based classification
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
An empirical evaluation on dimensionality reduction schemes for dissimilarity-based classifications
Pattern Recognition Letters
One-sided prototype selection on class imbalanced dissimilarity matrices
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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The aim of this paper is to present a strategy by which a new philosophy for pattern classification, namely that pertaining to dissimilarity-based classifiers (DBCs), can be efficiently implemented. This methodology, proposed by Duin and his co-authors (see Refs. [Experiments with a featureless approach to pattern recognition, Pattern Recognition Lett. 18 (1997) 1159-1166; Relational discriminant analysis, Pattern Recognition Lett. 20 (1999) 1175-1181; Dissimilarity representations allow for buillding good classifiers, Pattern Recognition Lett. 23 (2002) 943-956; Dissimilarity representations in pattern recognition, Concepts, theory and applications, Ph.D. Thesis, Delft University of Technology, Delft, The Netherlands, 2005; Prototype selection for dissimilarity-based classifiers, Pattern Recognition 39 (2006) 189-208]), is a way of defining classifiers between the classes, and is not based on the feature measurements of the individual patterns, but rather on a suitable dissimilarity measure between them. The advantage of this methodology is that since it does not operate on the class-conditional distributions, the accuracy can exceed the Bayes' error bound. The problem with this strategy is, however, the need to compute, store and process the inter-pattern dissimilarities for all the training samples, and thus, the accuracy of the classifier designed in the dissimilarity space is dependent on the methods used to achieve this. In this paper, we suggest a novel strategy to enhance the computation for all families of DBCs. Rather than compute, store and process the DBC based on the entire data set, we advocate that the training set be first reduced into a smaller representative subset. Also, rather than determine this subset on the basis of random selection, or clustering, etc., we advocate the use of a prototype reduction scheme (PRS), whose output yields the points to be utilized by the DBC. The rationale for this is explained in the paper. Apart from utilizing PRSs, in the paper we also propose simultaneously employing the Mahalanobis distance as the dissimilarity-measurement criterion to increase the DBCs classification accuracy. Our experimental results demonstrate that the proposed mechanism increases the classification accuracy when compared with the ''conventional'' approaches for samples involving real-life as well as artificial data sets-even though the resulting dissimilarity criterion is not symmetric.