首页|Study Data from University Pablo de Olavide Update Understanding of Artificial I ntelligence (A New Approach Based On Association Rules To Add Explainability To Time Series Forecasting Models)
Study Data from University Pablo de Olavide Update Understanding of Artificial I ntelligence (A New Approach Based On Association Rules To Add Explainability To Time Series Forecasting Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Artificial Intelligence. According to news reporting from Seville, Spain, by New sRx journalists, research stated, “Machine learning and deep learning have becom e the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it i s widely known that some of these solutions based on artificial intelligence are considered black -box models, meaning that most experts find difficult to expla in and interpret the models and why they generate such outputs.” Funders for this research include Spanish Government, Junta de Andalucia, Univer sidad Pablo de Olavide/CBUA. The news correspondents obtained a quote from the research from University Pablo de Olavide, “In this context, explainable artificial intelligence is emerging w ith the aim of providing black -box models with sufficient interpretability. Thu s, models could be easily understood and further applied. This work proposes a n ovel method to explain black -box models, by using numeric association rules to explain and interpret multi -step time series forecasting models. Thus, a multi -objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable.”
SevilleSpainEuropeArtificial Intel ligenceEmerging TechnologiesMachine LearningUniversity Pablo de Olavide
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