Gupta, Prashant K and Sharma, Deepak and Andreu-Perez, Javier (2023) A Perceptual Computing Approach for Learning Interpretable Unsupervised Fuzzy Scoring Systems. IEEE Transactions on Artificial Intelligence, 5 (8). pp. 3832-3844. DOI https://doi.org/10.1109/tai.2023.3333762
Gupta, Prashant K and Sharma, Deepak and Andreu-Perez, Javier (2023) A Perceptual Computing Approach for Learning Interpretable Unsupervised Fuzzy Scoring Systems. IEEE Transactions on Artificial Intelligence, 5 (8). pp. 3832-3844. DOI https://doi.org/10.1109/tai.2023.3333762
Gupta, Prashant K and Sharma, Deepak and Andreu-Perez, Javier (2023) A Perceptual Computing Approach for Learning Interpretable Unsupervised Fuzzy Scoring Systems. IEEE Transactions on Artificial Intelligence, 5 (8). pp. 3832-3844. DOI https://doi.org/10.1109/tai.2023.3333762
Abstract
Scoring the driver’s behavior through the analysis of his/ her road trip data is an active area of research. However, such systems suffer from a lack of explainability, integration of expert bias in the calculated score, and ignoring the semantic uncer- tainty of various variables contributing to the score. To overcome these limitations, we have proposed a novel perceptual computing based unsupervised scoring system. The prowess of the proposed system has been exemplified in a case study of driver’s scoring from telemetry data. Our proposed approach yields scores that showed a higher significant separability between drivers performing responsible or irresponsible (aggressive or drowsy) driving behaviours, than the formal method of computing these scores (a p value of 3.94 × 10¯⁴ and 3.42 × 10¯³, respectively, in a Kolmogorov-Smirnov test). Further, the proposed method displayed higher robustness in the bootstrap test (where 30% of original data was omitted at random) by providing scores that were 90% similar to the original ones for all results within a confidence interval of 95%.
Item Type: | Article |
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Uncontrolled Keywords: | Computing with Words, Fuzzy Logic, Perceptual Computing Systems, Unsupervised Scoring Systems |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 21 Nov 2023 13:47 |
Last Modified: | 21 Aug 2024 19:21 |
URI: | http://repository.essex.ac.uk/id/eprint/36859 |
Available files
Filename: preprint_manuscript.pdf