Towards Explainable Sequential Learning
This addresses the need for explainable AI in sequential learning, particularly for multivariate time series classification, though it appears incremental as it builds on existing event-based literature.
The paper tackles the problem of making sequential learning more explainable by proposing EMeriTAte+DF, a hybrid pipeline that bridges numerical-driven temporal data classification with event-based approaches. The method outperforms state-of-the-art solutions for multivariate time series classification.
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.