Cloud guided stream classification using class-based ensemble

Tahseen M. Al-Khateeb, Mohammad M. Masud, Latifur Khan, Bhavani Thuraisingham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

We propose a novel class-based micro-classifier ensemble classification technique (MCE) for classifying data streams. Traditional ensemble-based data stream classification techniques build a classification model from each data chunk and keep an ensemble of such models. Due to the fixed length of the ensemble, when a new model is trained, one existing model is discarded. This creates several problems. First, if a class disappears from the stream and reappears after a long time, it would be misclassified if a majority of the classifiers in the ensemble does not contain any model of that class. Second, discarding a model means discarding the corresponding data chunk completely. However, knowledge obtained from some classes might be still useful and if they are discarded, the overall error rate would increase. To address these problems, we propose an ensemble model where each class information is stored separately. From each data chunk, we train a model for each class of data. We call each such model a micro-classifier. This approach is more robust than existing chunk-based ensembles in handling dynamic changes in the data stream. To the best of our knowledge, this is the first attempt to classify data streams using the class-based ensembles approach. When the number of classes grow in the stream, class-based ensembles may degrade in performance (speed). Hence, we sketch a cloud-based solution of our class-based ensembles to handle a large number of classes effectively. We compare our technique with several state-of-the-art data stream classification techniques on both synthetic and benchmark data streams, and obtain much higher accuracy.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
Pages694-701
Number of pages8
DOIs
Publication statusPublished - Oct 2 2012
Externally publishedYes
Event2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012 - Honolulu, HI, United States
Duration: Jun 24 2012Jun 29 2012

Publication series

NameProceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012

Other

Other2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
Country/TerritoryUnited States
CityHonolulu, HI
Period6/24/126/29/12

Keywords

  • classification MapReduce Ensemble cloud

ASJC Scopus subject areas

  • Software

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