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

TL;DR

This paper introduces a fuzzy inference system using TSK+ and CFS to predict IoT device positions with unseen labels, addressing a gap in traditional ML models that cannot handle unseen classes.

Contribution

It presents a novel fuzzy inference approach combined with feature selection for predicting unseen IoT device positioning labels, enhancing model generalization.

Findings

High prediction accuracy for unseen labels

Effective handling of large number of continuous classes

Demonstrated efficiency in IoT device positioning

Abstract

Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades. However, unseen class label prediction is far less explored due to missing classes being invisible in training ML or DL models. In this work, we propose a fuzzy inference system to cope with such a challenge by adopting TSK+ fuzzy inference engine in conjunction with the Curvature-based Feature Selection (CFS) method. The practical feasibility of our system has been evaluated by predicting the positioning labels of networking devices within the realm of the Internet of Things (IoT). Competitive prediction performance confirms the efficiency and efficacy of our system, especially when a large number of continuous class labels are unseen during the model training stage.