Abstract

Current research in data mining concentrates on the development of new techniques for mining high-speed data streams. The fundamental data generation mechanism changes over the time, this is common in most real-world data streams, which introduces concept drift into the data. Mobile devices, streaming, remote sensing applications which are networked digital information systems, encounter the issue of the size of data and the capacity to be adaptive to changes in concept in real-time. In this paper the main issue of concept drift is addressed with real and synthetic data streams and the comparison of ensemble classifiers has been made in view of concept drift for the assessment of the performance. Various classifiers were applied on data stream with and without concept drift for analysis. This has resulted in better performance of the classifiers on the type of data whether it is categorical, numeric or alphanumeric.

References

• Kadwe Y. and Suryawansh V., 2015 A Review on Concept Drift, IOSR Journal of Computer Engineering, (JAN-FEB. 2015), 20-26.
• Gama J. Zliobait I. Bifet A. and Pechnizkiy M., 2013 A Survey on Concept Drift Adaptation, ACM Computing Surveys, (JAN. 2013).
• Mittal V. and Kashyap I., 2015 Online Methods of Learning in Occurrence of Concept Drift, International Journal of Computer Applications, (MAY. 2015), 0975 – 8887.
• Bifet A., Read J., Pfahringer B., Holmes G. and Zliobait I., 2013 CD-MOA: Change Detection Framework for Massive Online Analysis, Springer, 92-103.
• Hoeglinger S., Pears R. and Koh Y., 2009 CBDT: A concept based approach to data stream, Researchgate, (APRIL 2009).
• Bifet A. Read J. , Morales G., and Pfahringer G., 2015 Efficient Online Evaluation of Big Data Stream Classifiers, ACM, (AUG. 2015).
• Wankhade K. and Dongre S., 2012 A New Adaptive Ensemble Boosting Classifier for Concept Drifting Stream Data, International Journal of Modeling and Optimization, (AUG 2012), 493-497.
• Devasena L., 2014 Efficiency Comparison of Multilayer Perceptron and SMO Classifier for Credit Risk Prediction, Intl J of Advanced Research in Computer and Communication Engineering, (APRIL 2014), 6155-6162.
• Cohen E. and Strauss M., 2003 Maintaining time decaying stream aggregates, Proceedings of the 22th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, San Diego, California, U.S.A., (JUNE 2003), 223-233.
• Trambadiya T. and Bhanodia P., 2013 A heuristic approach to preserve privacy in stream data with classification, Intl J of Engineering Research and Applications, 1096-1103.
• Pramod S. and Vyas P., 2012 Data stream mining: a review on windowing approach, Global Journal of computer science and technology software and data engineering, 27-30.
• Chhinkaniwala H., Patel K. and Garg S., 2012 Privacy preserving data stream classification using data perturbation techniques, Intl Conf on Emerging Trends in Electrical, Electronics and Communication Technologies, pp. 1-8.
• Li S., Hong L. and Zhen S., 2011 A new classification algorithm for data stream, Intl J Modern Education and Computer Science, 32-39.
• Ringne A.G., Sood D. and Toshniwal D. 2011 Compression and privacy preservation of data streams using moments, Intl J of machine learning and computing, 473-478.
• Benjamin M. Fung , Wang K. , and Philip S., 2007 Anonymizing classification data for privacy preservation, IEEE Trans on Knowledge And Data Engineering, 711-725.
• Aggarwal C. and Philip Y., 2008 A general survey of privacy-preserving data mining models and algorithms, Springer, 11-52.
• Street W. and Kim Y., 2001 A streaming ensemble algorithm (SEA) for large-scale classification, In KDD 01 New York, NY, USA, ACM Press, 377-382.
• Babcock B., Babu S., Datar M., Motwani R. and Widom J., 2002 Models and Issues in Data Stream Systems, ACM PODS Conference.
• http://moa.cms.waikato.ac.nz/