Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers

Vissol-Gaudin, E.; Kotsialos, A.; Groves, C.; Pearson, C.; Zeze, D.A.; Petty, M.C.; Al-moubayed, N.

Abstract

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.

Citation

Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018, July). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. Presented at 2018 IEEE World Congress on Computational Intelligence (WCCI 2018)., Rio de Janeiro, Brazil

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2018 IEEE World Congress on Computational Intelligence (WCCI 2018).
• Start Date: Jul 8, 2018
• End Date: Jul 13, 2018
• Acceptance Date: Mar 15, 2018
• Online Publication Date: Oct 4, 2018
• Publication Date: Oct 4, 2018
• Deposit Date: Jun 1, 2018
• Publicly Available Date: Jun 1, 2018
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 646-653
• Book Title: 2018 IEEE Congress on Evolutionary Computation (CEC) : 8-13 July 2018, Rio de Janeiro, Brazil ; proceedings.
• ISBN: 9781509060184
• DOI: https://doi.org/10.1109/cec.2018.8477779
• Public URL: https://durham-repository.worktribe.com/output/1144657