Evolving Feature Selection: Synergistic Backward and Forward Deletion Method Utilizing Global Feature Importance

Abstract

Explainable artificial intelligence (XAI) techniques are used to understand the rationale behind the decision-making of machine learning models. In addition to the need for model explainability, the demand for an ever-growing number of multimodal features has dramatically increased model complexity. This underscores the importance of precise feature selection to ensure high model accuracy. Using our Approximate Inverse Model Explanations (AIME) technique, which currently presents the best XAI capability in the field, this study incorporated a novel backward and forward deletion process. This pre-assesses global feature importance by calculating and ordering their AIME-reported global importance. Through the backward deletion process, it assesses model accuracy by progressively eliminating less important features, resulting in a feature set configuration that guarantees the highest model accuracy. Then, the forward deletion process further refines the feature set by discarding the least important features until the he model’s accuracy declines, which reduces the computational burden and ensures optimal performance. We applied our method to the detailed and expansive Multimodal Emotion Line dataset and leveraged 4,870 facial, voice, and spoken language features in the Google Colab Pro+ environment to demonstrate AIME’s efficacy in enabling researchers to maximize both model explainability and performance: the holy grail of XAI.

Keywords

• Approximate inverse model explanations (AIME)
• backward and forward deletion
• explainable artificial intelligence (XAI)
• feature selection
• global feature importance