Online Capricious Data Stream Learning with Sparse Labels

With the dramatic increase in data volume,machine learning methods have gradually transitioned from traditional static learning to online learning modes that are designed for streaming data.Capricious data streams refer to data instances arriving over time in a sequential manner,where the feature space can potentially undergo capricious changes.It means that old features may disappear at any time,while new features may emerge.For example,in the field of environmental monitoring,the addition of new sensors or sudden anomalies in existing sensors can cause arbitrary changes in the feature space of the data stream.Furthermore,existing online learning methods for data streams often assume access to the true labels of all data instances.However,in real-world applications,data labeling is often sparse due to the high cost of manual annotation.Therefore,to address the problem of online learning in capricious data streams with sparse labels,a passive-active learning-based online learning algorithm called PAACDS(Passive Aggressive Active Learning for Capricious Data Streams),along with its variant PAACDS-I,is proposed.Firstly,an online active learning method is utilized to select valuable data instances,allowing the construction of superior prediction models with minimal supervision.Subsequently,after obtaining the queried labels for the selected data instances,the dynamic classifier,which encompasses the shared and newly added feature spaces in the capricious data streams,is updated using online passive-active update rules and the principle of boundary maximization.Finally,the proposed algorithm is compared to existing state-of-the-art methods on twelve datasets.Extensive experimental comparisons and analyses validate the effectiveness of the proposed algorithm in scenarios involving capricious data streams and sparse labels.