XCS-IMG is the adaptation of accuracy based learning classifier system (XCS) for images. It is a step towards explainable artificial intelligence (XAI).We have demonstrated how the evolved rules can be visualized to explain the classification result. The system uses concept of filters that are evolved during the learning phase to capture image features that can be visualized.
Image classification is one of the most important and widely explored problems in machine learning. Deep learning has shown promising results for image classification; however, the deep learning-based methods perform classification based on the black box CNNs, which lack transparency and explainability. The ability to rationalize the decision-making of a system is increasingly becoming important, for example, due to the critical nature of some decisions, and to move towards explainable artificial intelligence (XAI). Evolutionary machine learning, on the other hand, has the advantage of being transparent and hence suitable for designing explainable systems. This study serves as the proof of concept for designing such a system using evolutionary machine learning. We have developed an approach, based on the rule-based learning classifier systems (LCSs). We have demonstrated how the evolved rules can be visualized to explain the classification result. Comparisons have been done with existing evolutionary machine learning approaches for accuracy and explainability. We believe that our approach will open new avenues of research for explainable image classification systems.
