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dc.contributor.authorFarid, D.M.*
dc.contributor.authorZhang, L.*
dc.contributor.authorHossain, A.*
dc.contributor.authorRahman, C.M.*
dc.contributor.authorStrachan, R.*
dc.contributor.authorSexton, G.*
dc.contributor.authorDahal, Keshav P.*
dc.date.accessioned2016-10-07T13:08:01Z
dc.date.available2016-10-07T13:08:01Z
dc.date.issued2013
dc.identifier.citationFarid DM, Zhang L, Hossain A et al (2013) An adaptive ensemble classifier for mining concept drifting data streams. Expert systems with Applications. 40(15): 5895-5906.
dc.identifier.urihttp://hdl.handle.net/10454/9573
dc.descriptionNo
dc.description.abstractIt is challenging to use traditional data mining techniques to deal with real-time data stream classifications. Existing mining classifiers need to be updated frequently to adapt to the changes in data streams. To address this issue, in this paper we propose an adaptive ensemble approach for classification and novel class detection in concept drifting data streams. The proposed approach uses traditional mining classifiers and updates the ensemble model automatically so that it represents the most recent concepts in data streams. For novel class detection we consider the idea that data points belonging to the same class should be closer to each other and should be far apart from the data points belonging to other classes. If a data point is well separated from the existing data clusters, it is identified as a novel class instance. We tested the performance of this proposed stream classification model against that of existing mining algorithms using real benchmark datasets from UCI (University of California, Irvine) machine learning repository. The experimental results prove that our approach shows great flexibility and robustness in novel class detection in concept drifting and outperforms traditional classification models in challenging real-life data stream applications. (C) 2013 Elsevier Ltd. All rights reserved.
dc.subjectAdaptive ensembles
dc.subject; Concept drift
dc.subject; Clustering
dc.subject; Data streams
dc.subject; Decision trees
dc.subject; Novel classes
dc.subject; Evolving data streams
dc.subject; Novelty detection
dc.subject; Trees
dc.titleAn adaptive ensemble classifier for mining concept drifting data streams
dc.status.refereedYes
dc.typeArticle
dc.type.versionNo full-text available in the repository
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2013.05.001


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