An adaptive ensemble classifier for mining concept drifting data streams

Farid, Dewan, Zhang, Li, Hossain, Alamgir, Rahman, Chowdhury, Strachan, Rebecca, Sexton, Graham and Dahal, Keshav (2013) An adaptive ensemble classifier for mining concept drifting data streams. Expert Systems with Applications, 40 (15). pp. 5895-5906. ISSN 0957-4174

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It 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.

Item Type: Article
Uncontrolled Keywords: Adaptive ensembles, concept drift, clustering, data streams, decision trees, novel classes
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ay Okpokam
Date Deposited: 16 Jul 2013 15:59
Last Modified: 13 Oct 2019 00:32

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