NDLI: An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams

Content Provider	Springer Nature Link
Author	Hosseini, Mohammad Javad Gholipour, Ameneh Beigy, Hamid
Copyright Year	2015
Abstract	Recent advances in storage and processing have provided the possibility of automatic gathering of information, which in turn leads to fast and continuous flows of data. The data which are produced and stored in this way are called data streams. Data streams are produced in large size, and much dynamism and have some unique properties which make them applicable to model many real data mining applications. The main challenge of streaming data is the occurrence of concept drift. In addition, regarding the costs of labeling of instances, it is often assumed that only a small fraction of instances are labeled. In this paper, we propose an ensemble algorithm to classify instances of non-stationary data streams in a semi-supervised environment. Furthermore, this method is intended to recognize recurring concept drifts of data streams. In the proposed algorithm, a pool of classifiers is maintained by the algorithm with each classifier being representative of one single concept. At first, a batch of instances is classified by the algorithm. Thereafter, some of these instances are labeled and this partially labeled batch is used to update the classifiers in the pool. This process repeats for consecutive batches of the streams. The main advantage of the algorithm is that it uses unlabeled instances as well as labeled ones in the learning task. Experimental results show the effectiveness of the proposed algorithm over the state-of-the-art methods, in different aspects.
Starting Page	567
Ending Page	597
Page Count	31
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	46
Issue Number	3
e-ISSN	02193116
Language	English
Publisher	Springer London
Publisher Date	2015-04-28
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Semi-supervised learning Ensemble learning Data streams Concept drift Cluster assumption Information Systems and Communication Service IT in Business
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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