Lab Assignments 05/04/2011

• Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints

  This paper appears in: Knowledge and Data Engineering, IEEE Transactions on Issue Date: June 2011 Volume: 23 Issue:6 On page(s): 859 - 874 ISSN: 1041-4347 Digital Object Identifier: 10.1109/TKDE.2010.61 Date of Publication: 22 aprile 2010 Date of Current Version: 21 aprile 2011 Sponsored by: IEEE Computer Society ABSTRACTMost 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.

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