A framework for vehicle quality evaluation based on interpretable machine learning

Mohammad Alwadi, Girija Chetty, Mohammad Yamin

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Ensuring high quality of a vehicle will increase the lifetime and customer experience, in addition to the maintenance problems, and it is important that there are objective scientific methods available, for evaluating the quality of the vehicle. In this paper, we present a computational framework for evaluating the vehicle quality based on interpretable machine learning techniques. The validation of the proposed framework for a publicly available vehicle quality evaluation dataset has shown an objective machine learning based approach with improved interpretability and deep insight, by using several post-hoc model interpretability enhancement techniques.

Original languageEnglish
Pages (from-to)129-136
Number of pages8
JournalInternational Journal of Information Technology (Singapore)
Volume15
Issue number1
DOIs
Publication statusPublished - Jan 2023

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