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Entropy 2017, 19(5), 216; doi:10.3390/e19050216

Designing Labeled Graph Classifiers by Exploiting the Rényi Entropy of the Dissimilarity Representation

Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
Academic Editors: Jose C. Principe and Badong Chen
Received: 6 April 2017 / Revised: 26 April 2017 / Accepted: 6 May 2017 / Published: 9 May 2017
(This article belongs to the Special Issue Entropy in Signal Analysis)
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Abstract

Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various datasets of labeled graphs. However, the design of effective learning procedures operating in the space of labeled graphs is still a challenging problem, especially from the computational complexity viewpoint. In this paper, we present a major improvement of a general-purpose classifier for graphs, which is conceived on an interplay between dissimilarity representation, clustering, information-theoretic techniques, and evolutionary optimization algorithms. The improvement focuses on a specific key subroutine devised to compress the input data. We prove different theorems which are fundamental to the setting of the parameters controlling such a compression operation. We demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well-known datasets of labeled graphs, considering as distinct performance indicators the classification accuracy, computing time, and parsimony in terms of structural complexity of the synthesized classification models. The results show state-of-the-art standards in terms of test set accuracy and a considerable speed-up for what concerns the computing time. View Full-Text
Keywords: graph-based pattern recognition; classification of labeled graphs; dissimilarity representation; information-theoretic data characterization graph-based pattern recognition; classification of labeled graphs; dissimilarity representation; information-theoretic data characterization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Livi, L. Designing Labeled Graph Classifiers by Exploiting the Rényi Entropy of the Dissimilarity Representation. Entropy 2017, 19, 216.

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