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.

An Integration-Based Approach to Pattern Clustering and Classification / Sani, L.; D'Addese, G.; Pecori, R.; Mordonini, M.; Villani, M.; Cagnoni, S.. - 11298:(2018), pp. 362-374. (Intervento presentato al convegno 17th Conference of the Italian Association for Artificial Intelligence, AI*IA 2018 tenutosi a ita nel 2018) [10.1007/978-3-030-03840-3_27].

An Integration-Based Approach to Pattern Clustering and Classification

D'Addese G.;Villani M.;
2018

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.
2018
9-nov-2018
17th Conference of the Italian Association for Artificial Intelligence, AI*IA 2018
ita
2018
11298
362
374
Sani, L.; D'Addese, G.; Pecori, R.; Mordonini, M.; Villani, M.; Cagnoni, S.
An Integration-Based Approach to Pattern Clustering and Classification / Sani, L.; D'Addese, G.; Pecori, R.; Mordonini, M.; Villani, M.; Cagnoni, S.. - 11298:(2018), pp. 362-374. (Intervento presentato al convegno 17th Conference of the Italian Association for Artificial Intelligence, AI*IA 2018 tenutosi a ita nel 2018) [10.1007/978-3-030-03840-3_27].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1190092
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