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Confidence measures for carbon-nanotube / liquid crystals classifiers.

Vissol-Gaudin, E. and Kotsialos, A. and Groves, C. and Pearson, C. and Zeze, D.A. and Petty, M.C. and Al-moubayed, N. (2018) 'Confidence measures for carbon-nanotube / liquid crystals classifiers.', in 2018 IEEE Congress on Evolutionary Computation (CEC) : 8-13 July 2018, Rio de Janeiro, Brazil ; proceedings. Piscataway: IEEE, pp. 646-653.


This paper focuses on a performance analysis of single-walled-carbon-nanotube / liquid crystal classifiers produced by evolution in materio. A new confidence measure is proposed in this paper. It is different from statistical tools commonly used to evaluate the performance of classifiers in that it is based on physical quantities extracted from the composite and related to its state. Using this measure, it is confirmed that in an untrained state, ie: before being subjected to an algorithm-controlled evolution, the carbon-nanotube-based composites classify data at random. The training, or evolution, process brings these composites into a state where the classification is no longer random. Instead, the classifiers generalise well to unseen data and the classification accuracy remains stable across tests. The confidence measure associated with the resulting classifier's accuracy is relatively high at the classes' boundaries, which is consistent with the problem formulation.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:15 March 2018
Date deposited:01 June 2018
Date of first online publication:04 October 2018
Date first made open access:No date available

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