Abstract
The main challenges of data streams classification include infinite length, concept-drifting, arrival of novel classes and lack of labeled instances. Most existing techniques address only some of them and ignore others. So an ensemble classification model based on decision-feedback (ECM-BDF) is presented in this paper to address all these challenges. Firstly, a data stream is divided into sequential chunks and a classification model is trained from each labeled data chunk. To address the infinite length and concept-drifting problem, a fixed number of such models constitute an ensemble model E and subsequent labeled chunks are used to update E. To deal with the appearance of novel classes and limited labeled instances problem, the model incorporates a novel class detection mechanism to detect the arrival of a novel class without training E with labeled instances of that class. Meanwhile, unsupervised models are trained from unlabeled instances to provide useful constraints for E. An extended ensemble model Ex can be acquired with the constraints as feedback information, and then unlabeled instances can be classified more accurately by satisfying the maximum consensus of Ex. Experimental results demonstrate that the proposed ECM-BDF outperforms traditional techniques in classifying data streams with limited labeled data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: The Journal of China Universities of Posts and Telecommunications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.