Fuzzy networks for explainable artificial intelligence

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Abstract

Advanced machine learning techniques are very powerful in predictive tasks. However, they are mostly weak in explaining the inference process and they are mostly treated as black-box models. Fuzzy Network (FN) is powerful white-box technique which is capable of dealing with complexity and linguistic uncertainty. In this paper, a method is introduced to optimise Rule Based Networks using Fuzzy C-Means (FCM) for rule reduction, Genetic Algorithms to tune the membership functions and Backward Selection to reduce the inputs and network branches. A case study in transport and telecommuting is used to illustrate the performance of the proposed method. The results show the FN ability to explain the internal process of decision making and its capabilities in transparency and interpretability as an Explainable AI method.
Original languageEnglish
Title of host publicationFuzzy Networks for Explainable Artificial Intelligence
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages2
ISBN (Electronic)9798350339857
ISBN (Print)9798350339840
DOIs
Publication statusPublished - 2 Aug 2023
EventIEEE CAI2023: Conference on Artificial Intelligence - Santa Clara, United States
Duration: 5 Jun 20236 Jun 2023
https://cai.ieee.org/2023/

Conference

ConferenceIEEE CAI2023: Conference on Artificial Intelligence
Country/TerritoryUnited States
CitySanta Clara
Period5/06/236/06/23
Internet address

Keywords

  • Fuzzy Rule Based Network
  • Fuzzy Network Optimization
  • White-Box Model
  • Explainable AI

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