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
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
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
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Optimizing dissimilarity-based classifiers using a newly modified hausdorff distance
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
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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 [3], [4], [5], [6], [8]), 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 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. Apart from utilizing PRSs, in the paper we also propose simultaneously employing the Mahalanobis distance as the dissimilarity-measurement criterion to increase the DBC’s 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.