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SVM based Feature Extraction for Novel Class Detection from Streaming Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 110 - Number 9 |
| Year of Publication: 2015 |
| Authors: Arati Kale, M.d.ingle |
10.5120/19341-9762
|
Arati Kale, M.d.ingle . SVM based Feature Extraction for Novel Class Detection from Streaming Data. International Journal of Computer Applications. 110, 9 ( January 2015), 1-3. DOI=10.5120/19341-9762
Abstract
World have huge amount of data. Data stream classification contain several problem such as Infinite Length , Concept Drift ,Concept Evolution and Feature Evolution. Infinite Length means data available in huge amount and it is difficult to store all historical data for training. Concept Evolution occurs as a result of new classes evolving in stream. Concept Drift occurs as a result of changes in underlying concepts. Feature Evolution occurs as new feature arises. Traditional data stream classifier only addresses Infinite Length and Concept Drift. In this paper we propose ensemble classification framework where each classifier is equipped with novel class detector to address Concept Drift and Concept Evolution. Also increases accuracy of novel class detection techniques by using SVM based polynomial kernel.
References
- Khan, L. Tools, "Data Stream Mining: Challenges and Techniques", Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on22010.
- Farid, D. M. ; Rahman C. M. ," Novel class detection in concept-drifting data stream mining employing decision tree", Electrical & Computer Engineering (ICECE), 2012 7th International Conference on2012.
- E. J. Spinosa, A. P. de Leon F. de Carvalho, and J. Gama, "Cluster-Based Novel Concept Detection in Data Streams Applied to Intrusion Detection in Computer Networks," Proc. ACM Symp. Applied Computing (SAC), pp. 976-980, 2008.
- M. M. Masud, Q. Chen, J. Gao, L. Khan, J. Han, and B. M. Thuraisingham, "Classification And Novel Class Detection of Data Streams in a Dynamic Feature Space," Proc. European Conf. Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 337-352,2010.
- Peng Zhang; Xingquan Zhu; Li Guo, "Mining Data Streams with Labeled and Unlabeled Training Examples", Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on2009.
- M. M. Masud, J. Gao, L. Khan, J. Han, and B. M. ,Thuraisingham,"Integrating Novel Class Detection with Classification for Concept Drifting Data Streams," Proc. European Conf. Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 79-94,2009.
- M. M. Masud, J. Gao, L. Khan, J. Han, and B. M. Thuraisingham,"Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints," IEEE Trans. Knowledge and Data Eng. , vol. 23, no. 6, pp. 859-874, June 2011.
- M. M. Masud, Q. Chen, L. Khan, C. Aggarwal, J. Gao, J. Han, and B. M. Thuraisingham, "Addressing Concept-Evolution in Concept-Drifting Data Streams," Proc. IEEE Int'l Conf. Data Mining (ICDM),pp. 929-934, 2010.
- Mohammad M. Masud, Qing Chen, Latifur Khan, Charu C. Aggarwal, "Classification and Adaptive Novel Class Detection of Feature-Evolving Data Streams" IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 7, JULY 2013.
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