We propose a novel way of assessing and fusing noisy dynamic data using a Tsetlin Machine. Our approach consists in monitoring how explanations in form of logical clauses that a TM learns changes with possible noise in dynamic data. This way TM can recognize the noise by lowering weights of previously learned clauses, or reflect it in the form of new clauses. We also perform a comprehensive experimental study using notably different datasets that demonstrated high performance of the proposed approach.

翻译：我们提出了一种利用Tsetlin Machine评估与融合含噪动态数据的新方法。该方法通过监测Tsetlin Machine学习到的逻辑子句（即解释形式）随动态数据中潜在噪声的变化，实现噪声识别。具体而言，TM可通过降低先前学习子句的权重来适应噪声，或通过构建新子句反映噪声影响。我们在多个具有显著差异的数据集上开展了全面实验研究，结果表明所提方法具有优异性能。