Issue No. 07 - July (2013 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.109
Mohammad M. Masud , United Arab Emirates University, Al-Ain
Qing Chen , China National Petroleum Company, Beijing
Latifur Khan , University of Texas at Dallas, Richardson
Charu C. Aggarwal , IBM T.J. Watson Research Center, Hawthorne
Jing Gao , University of Illinois at Urbana-Champaign, Urbana
Jiawei Han , University of Illinois at Urbana-Champaign, Urbana
Ashok Srivastava , NASA Ames Research Center, Moffett Field
Nikunj C. Oza , NASA Ames Research Center, Moffett Field
Data stream classification poses many challenges to the data mining community. In this paper, we address four such major challenges, namely, infinite length, concept-drift, concept-evolution, and feature-evolution. Since a data stream is theoretically infinite in length, it is impractical to store and use all the historical data for training. Concept-drift is a common phenomenon in data streams, which occurs as a result of changes in the underlying concepts. Concept-evolution occurs as a result of new classes evolving in the stream. Feature-evolution is a frequently occurring process in many streams, such as text streams, in which new features (i.e., words or phrases) appear as the stream progresses. Most existing data stream classification techniques address only the first two challenges, and ignore the latter two. In this paper, we propose an ensemble classification framework, where each classifier is equipped with a novel class detector, to address concept-drift and concept-evolution. To address feature-evolution, we propose a feature set homogenization technique. We also enhance the novel class detection module by making it more adaptive to the evolving stream, and enabling it to detect more than one novel class at a time. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach.
Feature extraction, Data models, Training, Knowledge engineering, Data engineering, Vocabulary, Heuristic algorithms, outlier, Data stream, concept-evolution, novel class
A. Srivastava et al., "Classification and Adaptive Novel Class Detection of Feature-Evolving Data Streams," in IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. , pp. 1484-1497, 2013.