Intuitionistic Fuzzy Cognitive Maps for Interpretable Image Classification

• URL: http://arxiv.org/abs/2408.03745v1
• Date: Wed, 7 Aug 2024 12:58:39 GMT
• Title: Intuitionistic Fuzzy Cognitive Maps for Interpretable Image Classification
• Authors: Georgia Sovatzidi, Michael D. Vasilakakis, Dimitris K. Iakovidis,
• Abstract summary: This paper introduces a novel framework, named Interpretable Intuitionistic FCM (I2FCM) which is domain-independent, simple to implement, and can be applied on CNN models. To the best of our knowledge this is the first time iFCMs are applied for image classification.
• Score: 2.130156029408832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The interpretability of machine learning models is critical, as users may be reluctant to rely on their inferences. Intuitionistic FCMs (iFCMs) have been proposed as an extension of FCMs offering a natural mechanism to assess the quality of their output through the estimation of hesitancy, a concept resembling to human hesitation in decision making. To address the challenge of interpretable image classification, this paper introduces a novel framework, named Interpretable Intuitionistic FCM (I2FCM) which is domain-independent, simple to implement, and can be applied on Convolutional Neural Network (CNN) models, rendering them interpretable. To the best of our knowledge this is the first time iFCMs are applied for image classification. Further novel contributions include: a feature extraction process focusing on the most informative image regions; a learning algorithm for data-driven determination of the intuitionistic fuzzy interconnections of the iFCM; an inherently interpretable classification approach based on image contents. In the context of image classification, hesitancy is considered as a degree of inconfidence with which an image is categorized to a class. The constructed iFCM model distinguishes the most representative image semantics and analyses them utilizing cause-and-effect relations. The effectiveness of the introduced framework is evaluated on publicly available datasets, and the experimental results confirm that it can provide enhanced classification performance, while providing interpretable inferences.

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