Abstract
Methods based on information theory, such as the Relevance Index (RI), have been employed to study complex systems for their ability to detect significant groups of variables, well integrated among one another and well separated from the others, which provide a functional block description of the system under analysis. The integration (or zI in its standardized form) is a metric that can express the significance of a group of variables for the system under consideration: the higher the zI, the more significant the group. In this paper, we use this metric for an unusual application to a pattern clustering and classification problem. The results show that the centroids of the clusters of patterns identified by the method are effective for distance-based classification algorithms. We compare such a method with other conventional classification approaches to highlight its main features and to address future research towards the refinement of its accuracy and computational efficiency.
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Notes
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Patterns obtained by both strategies are represented with the tags 005, 010, 015, 020 and 030, even if these numbers represent the actual percent noise levels only for the first set.
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Downloadable at ftp://ftp.ce.unipr.it/pub/cagnoni/license_plate.
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The authors would like to thank Chiara Lasagni for the many tests and for helping us reach full awareness of some of the finer details of the method.
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Sani, L., D’Addese, G., Pecori, R., Mordonini, M., Villani, M., Cagnoni, S. (2018). An Integration-Based Approach to Pattern Clustering and Classification. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds) AI*IA 2018 – Advances in Artificial Intelligence. AI*IA 2018. Lecture Notes in Computer Science(), vol 11298. Springer, Cham. https://doi.org/10.1007/978-3-030-03840-3_27
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