TY - GEN
T1 - A practical approach to classify evolving data streams
T2 - 8th IEEE International Conference on Data Mining, ICDM 2008
AU - Masud, Mohammad M.
AU - Gao, Jing
AU - Khan, Latifur
AU - Han, Jiawei
AU - Thuraisingham, Bhavani
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=67049160126&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67049160126&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2008.152
DO - 10.1109/ICDM.2008.152
M3 - Conference contribution
AN - SCOPUS:67049160126
SN - 9780769535029
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 929
EP - 934
BT - Proceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Y2 - 15 December 2008 through 19 December 2008
ER -