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Situating at the core of Artificial Intelligence (AI), Machine Learning (ML),and more specifically, Deep Learning (DL) have embraced great success in thepast two decades. However, unseen class label prediction is far less exploreddue to missing classes being invisible in training ML or DL models. In thiswork, we propose a fuzzy inference system to cope with such a challenge byadopting TSK+ fuzzy inference engine in conjunction with the Curvature-basedFeature Selection (CFS) method. The practical feasibility of our system hasbeen evaluated by predicting the positioning labels of networking deviceswithin the realm of the Internet of Things (IoT). Competitive predictionperformance confirms the efficiency and efficacy of our system, especially whena large number of continuous class labels are unseen during the model trainingstage.

Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase