Authors:
Han Xu
1
;
2
;
Zheming Zuo
3
;
Jie Li
4
and
Victor Chang
4
Affiliations:
1
Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China
;
2
School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
;
3
Department of Computer Science, Durham University, Durham DH1 3LE, U.K.
;
4
Cybersecurity, Information Systems and AI Research Group, School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS3 6DR, U.K.
Keyword(s):
Unseen Label Prediction, TSK+ Fuzzy Inference Engine, Curvature-based Feature Selection, Internet of Things, Networking 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.