Multi-label active learning from crowds for secure IIoT

With the development of IIoT (Industrial Internet of Things), Artificial Intelligence technology is widely used in many research areas, such as image classification, speech recognition, and information retrieval. Traditional supervised machine learning obtains labels from high-quality oracles, which is high cost and time-consuming and does not consider security. Since mull-label active learning becomes a hot topic, it is more challenging to train efficient and secure classification models, and reduce the label cost in the field of IIoT. To address this issue, this research focuses on the secure mull-label active learning for IIoT using an economical and efficient strategy called crowdsourcing, which involves querying labels from multiple low-cost annotators with various expertise on crowdsourcing platforms rather than relying on a high-quality oracle. To eliminate the effects of annotation noise caused by imperfect annotators, we propose the Mull-label Active Learning from Crowds (MALC) method, which uses a probabilistic model to simultaneously compute the annotation consensus and estimate the classifier's parameters while also taking instance similarly into account. Then, to actively choose the most informative instances and labels, as well as the most reliable annotators, an instance-labelannotator triplets selection technique is proposed. Experimental results on two real-world data sets show that the performance of MALC is superior to existing methods.

Automated summary

With the rise of IIoT, Artificial Intelligence is utilized in various research areas, and multi-label active learning has become popular. By utilizing crowdsourcing, a more economical and efficient strategy, for multi-label active learning in IIoT, the proposed MALC method outperforms existing techniques.