A practical approach to classify evolving data streams: Training with limited amount of labeled data

Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, Bhavani Thuraisingham

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

Abstract

Recent approaches in classifying evolving data streams are based on supervised learning algorithms, which can be trained with labeled data only. Manual labeling of data is both costly and time consuming. Therefore, in a real streaming environment, where huge volumes of data appear at a high speed, labeled data may be very scarce. Thus, only a limited amount of training data may be available for building the classification models, leading to poorly trained classifiers. We apply a novel technique to overcome this problem by building a classification model from a training set having both unlabeled and a small amount of labeled instances. This model is built as micro-clusters using semisupervised clustering technique and classification is performed with ℜ-nearest neighbor algorithm. An ensemble of these models is used to classify the unlabeled data. Empirical evaluation on both synthetic data and real botnet traffic reveals that our approach, using only a small amount of labeled data for training,outper forms state-of-the-art stream classification algorithms that use twenty times more labeled data than our approach.

Original languageEnglish (US)
Title of host publicationProceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Pages929-934
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italy
Duration: Dec 15 2008Dec 19 2008

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other8th IEEE International Conference on Data Mining, ICDM 2008
CountryItaly
CityPisa
Period12/15/0812/19/08

ASJC Scopus subject areas

  • Engineering(all)

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