Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163219
Title: Smart interpretable model (SIM) enabling subject matter experts in rule generation
Authors: Christianto, Hotman
Lee, Gary Kee Khoon
Zhou, Jair Weigui
Kasim, Henry
Rajan, Deepu
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Christianto, H., Lee, G. K. K., Zhou, J. W., Kasim, H. & Rajan, D. (2022). Smart interpretable model (SIM) enabling subject matter experts in rule generation. Expert Systems With Applications, 207, 117945-. https://dx.doi.org/10.1016/j.eswa.2022.117945
Journal: Expert Systems with Applications
Abstract: Current Artificial Intelligence (AI) technologies are widely regarded as black boxes, whose internal structures are not inherently transparent, even though they provide powerful prediction capabilities. Having a transparent model that enables users to understand its inner workings allows them to appreciate the learning and inference process, leading to trust and higher confidence in the model. While methods that help with interpretability have been created, most of them require the user to have a certain level of AI knowledge and do not allow a user to fine-tune them based on prior knowledge. In this paper, we present a smart interpretable model (SIM) framework that requires little to no AI knowledge and can be used to create a set of fuzzy IF-THEN rules along with its corresponding membership functions at ease. The framework also allows users to incorporate prior knowledge during various steps in the framework and generates comprehensive insights summarized from rules and samples, allowing users to identify anomalous rules, feature contributions of each sample and confidence level for each rule. We demonstrate these capabilities and compare our model to other existing rule-based models using various datasets that have been used for rule-based model validations. Validations are then done in terms of performance and whether the rules that are generated by SIM are similar to the rules generated by other more recent rule-based models.
URI: https://hdl.handle.net/10356/163219
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2022.117945
Schools: School of Computer Science and Engineering 
Research Centres: Rolls-Royce@NTU Corporate Lab 
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Page view(s)

77
Updated on Apr 11, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.