论文标题
看到看不见的：当模糊推理系统预测未在训练阶段出现的物联网设备定位标签
Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase
论文作者
论文摘要
位于人工智能（AI），机器学习（ML）的核心，更具体地说，深度学习（DL）在过去的二十年中取得了巨大的成功。但是，由于缺少类在训练ML或DL模型中，看不见的类标签预测的探索程度要小得多。在这项工作中，我们提出了一个模糊的推理系统，通过与基于曲率的特征选择（CFS）方法结合使用TSK+模糊推理引擎来应对这一挑战。通过预测物联网（IoT）领域内的网络设备的定位标签，已经评估了我们系统的实际可行性。竞争性预测性能证实了我们系统的效率和功效，尤其是在模型训练阶段看不见的大量连续类标签时。
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.
