Intuitionistic Fuzzy Cognitive Maps for Interpretable Image Classification

TL;DR

This paper introduces I2FCMs, a novel interpretable framework for image classification that combines intuitionistic fuzzy logic with feature extraction and can enhance the interpretability of deep learning models like CNNs.

Contribution

The paper presents the first application of intuitionistic fuzzy cognitive maps to image classification, including a new feature extraction and learning algorithm for interpretability.

Findings

Enhanced classification performance on public datasets.

Provides understandable explanations of predictions using linguistic terms.

Can be integrated with deep learning models to improve interpretability.

Abstract

Several deep learning (DL) approaches have been proposed to deal with image classification tasks. However, despite their effectiveness, they lack interpretability, as they are unable to explain or justify their results. To address the challenge of interpretable image classification, this paper introduces a novel framework, named Interpretable Intuitionistic Fuzzy Cognitive Maps (I2FCMs).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 human hesitation in decision making. In the context of image classification, hesitancy is considered as a degree of unconfidence with which an image is categorized to a class. To the best of our knowledge this is the first time iFCMs are applied for image classification. Further novel contributions of the introduced framework include the following: a) a feature extraction process focusing on the most informative image regions; b) a learning algorithm for automatic data-driven determination of the intuitionistic fuzzy interconnections of the iFCM, thereby reducing human intervention in the definition of the graph structure; c) an inherently interpretable classification approach based on image contents, providing understandable explanations of its predictions, using linguistic terms. Furthermore, the proposed I2FCM framework can be applied to DL models, including Convolutional Neural Network (CNN), rendering them interpretable. The effectiveness of I2FCM is evaluated on publicly available datasets, and the results confirm that it can provide enhanced classification performance, while providing interpretable inferences.