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Towards Explainable Sequential Learning
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DOI：10.2298/CSIS250303077B.png)
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Abstract
En 中文
This paper offers ahybridly 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 algorithms for multivariate time series classifications over four dataset considered in the present paper, thus showcasing the effectiveness of the proposed methodology premiering the extraction of explainable correlations across Multivariate Time Series (MTS) dimensions with dataful features.
Keywords:
Verified AI
eXplainable AI (XAI)
polyadic logs
Data Trends
Poly-DECLARE
Multivariate Time Series Classification
Journal
C
IF:
1.8
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