Towards Explainable Sequential Learning

2025-05-29

AI Summary

This work addresses the limited interpretability in multivariate time series classification by proposing EMeriTAte+DF, a hybrid interpretable pipeline integrating numeric-driven and event-driven paradigms. Methodologically, it introduces verification-and-validation (V&V) principles—previously unexplored in time series classification—enabling a posteriori interpretability: raw sequences are decomposed into concurrent events annotated with numeric payloads. We extend event mining theory to support higher-order concurrency modeling and design a specification mining algorithm for decision provenance. Experiments on multiple benchmark datasets demonstrate that EMeriTAte+DF surpasses state-of-the-art methods in both classification accuracy and explanation consistency. The framework establishes a novel paradigm for high-reliability time series decision-making, uniquely balancing predictive performance with human-understandable interpretability.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Security and Privacy: Data transparency and provenance

Abstract

This paper offers a hybrid explainable temporal data processing pipeline, DataFul Explainable MultivariatE coRrelatIonal Temporal Artificial inTElligence (EMeriTAte+DF), bridging numerical-driven temporal data classification with an event-based one through verified artificial intelligence principles, enabling human-explainable results. This was possible through a preliminary a posteriori explainable phase describing the numerical input data in terms of concurrent constituents with numerical payloads. This further required extending the event-based literature to design specification mining algorithms supporting concurrent constituents. Our previous and current solutions outperform state-of-the-art solutions for multivariate time series classifications, thus showcasing the effectiveness of the proposed methodology.

Problem

• Develops explainable sequential learning for temporal data
• Bridges numerical and event-based temporal data classification
• Outperforms state-of-the-art multivariate time series classification

Innovation

• Hybrid explainable temporal data processing pipeline
• A posteriori explainable phase with numerical payloads
• Extended event-based specification mining algorithms