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Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
June 2011 (vol. 23 no. 6)
pp. 859-874
ASCII Text
x 
 
Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, Bhavani Thuraisingham, "Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 6, pp. 859-874, June, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.61,
author = {Mohammad M. Masud and Jing Gao and Latifur Khan and Jiawei Han and Bhavani Thuraisingham},
title = {Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {23},
number = {6},
issn = {1041-4347},
year = {2011},
pages = {859-874},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.61},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
IS - 6
SN - 1041-4347
SP859
EP874
EPD - 859-874
A1 - Mohammad M. Masud,
A1 - Jing Gao,
A1 - Latifur Khan,
A1 - Jiawei Han,
A1 - Bhavani Thuraisingham,
PY - 2011
KW - Data streams
KW - concept-drift
KW - novel class
KW - ensemble classification
KW - K-means clustering
KW - k-nearest neighbor classification
KW - silhouette coefficient.
VL - 23
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
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.

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Index Terms:
Data streams, concept-drift, novel class, ensemble classification, K-means clustering, k-nearest neighbor classification, silhouette coefficient.
Citation:
Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, Bhavani Thuraisingham, "Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 6, pp. 859-874, June 2011, doi:10.1109/TKDE.2010.61
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