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Issue No. 06 - June (2011 vol. 23)
ISSN: 1041-4347
pp: 859-874
Mohammad M. Masud , University of Texas at Dallas, Richardson
Jing Gao , University of Illinois at Urbana Champaign , Urbana Urbana
Latifur Khan , University of Texas at Dallas, Richardson
Jiawei Han , Univ. of Illinois at Urbana-Champaign, Urbana
Bhavani Thuraisingham , The University of Texas at Dallas, Richardson
Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In order to determine whether an instance belongs to a novel class, the classification model sometimes needs to wait for more test instances to discover similarities among those instances. A maximum allowable wait time T_c is imposed as a time constraint to classify a test instance. Furthermore, most existing stream classification approaches assume that the true label of a data point can be accessed immediately after the data point is classified. In reality, a time delay T_l is involved in obtaining the true label of a data point since manual labeling is time consuming. We show how to make fast and correct classification decisions under these constraints and apply them to real benchmark data. Comparison with state-of-the-art stream classification techniques prove the superiority of our approach.
Data streams, concept-drift, novel class, ensemble classification, K-means clustering, k-nearest neighbor classification, silhouette coefficient.

L. Khan, J. Gao, J. Han, B. Thuraisingham and M. M. Masud, "Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 859-874, 2010.
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