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

Han Xu, Han Xu, Zheming Zuo, Jie Li, Victor Chang

2022

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.

Paper Citation

in Harvard Style

Xu H., Zuo Z., Li J. and Chang V. (2022). Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase. In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-564-7, pages 281-288. DOI: 10.5220/0011102800003194

in Bibtex Style

@conference{iotbds22,
author={Han Xu and Zheming Zuo and Jie Li and Victor Chang},
title={Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase},
booktitle={Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2022},
pages={281-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011102800003194},
isbn={978-989-758-564-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase
SN - 978-989-758-564-7
AU - Xu H.
AU - Zuo Z.
AU - Li J.
AU - Chang V.
PY - 2022
SP - 281
EP - 288
DO - 10.5220/0011102800003194