Concept drift detection with False Positive rate for multi-label classification in IoT data stream

Abstract

Machine learning, as a significant component of the Industrial Internet of Things (IIoT), has been widely applied in many fields. The continuously generated data from the various sensors are collected and stored, this is also known as a data stream. However, the non-stationary phenomenon in data stream, concept drift, is important to be detected immediately for the operation of the IoT system. Therefore, the detection method for concept drift is needed to alert the requirement to maintain or replace some components in advance, so as to avoid or mitigate the risk of malfunction of the IoT system. The majority of existing literature focuses on concept drift detection on binary classification. To fill this gap, here we propose an algorithm to detect multi-class. Moreover to improve the performance of detection, we also introduce an algorithm which integrates the existing error rate which is widely used with our newly proposed False Positive rate. The new method is called Drift Detection Method with False Positive rate for multi-label classification (DDM-FP-M). The DDM-FP-M firstly defines the false positive rate calculation method in multi-label classification, then integrates it with Drift Detection method with False positive rate (DDM-FP). The performance of the proposed method is evaluated through Intel Lab data and is found to outperform the Drift Detection method(DDM) over 50% cases.