Proposal of An Explainable Multi-class Classification Method using Neural Networks with ECOC Method

Abstract

With the advancement of information technology, data utilization has expanded, and multi-class classification tasks like image classification have become crucial. While machine learning models have achieved high accuracy, their opacity poses challenges, spurring the development of explainable AI (XAI). Current XAI methods, such as heatmaps highlighting influential input features or techniques quantifying feature importance for local explanations, primarily interpret input-output relationships. However, they fail to elucidate the structural relationships between multiple classes and provide limited global interpretability, often restricted to identifying predictive features. This study proposes a novel XAI approach leveraging the ECOC method to interpret category groupings that enhance model identification in multi-class classification. By decomposing the problem into multiple classification tasks, this approach offers insights into the ease of classification and the similarities among categories, advancing the interpretability of machine learning models.