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Reseach Article

Multi Novel Class Classification of Feature Evolving Data Streams with J48

by Punam D. Dhande, A.M. Dixit
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 11
Year of Publication: 2015
Authors: Punam D. Dhande, A.M. Dixit
10.5120/ijca2015905652

Punam D. Dhande, A.M. Dixit . Multi Novel Class Classification of Feature Evolving Data Streams with J48. International Journal of Computer Applications. 124, 11 ( August 2015), 31-36. DOI=10.5120/ijca2015905652

@article{ 10.5120/ijca2015905652,
author = { Punam D. Dhande, A.M. Dixit },
title = { Multi Novel Class Classification of Feature Evolving Data Streams with J48 },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 11 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number11/22150-2015905652/ },
doi = { 10.5120/ijca2015905652 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:14:09.498583+05:30
%A Punam D. Dhande
%A A.M. Dixit
%T Multi Novel Class Classification of Feature Evolving Data Streams with J48
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 11
%P 31-36
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the Data stream classification main issues are infinite length, concept drift, concept development, and feature development. Hypothetically data stream is infinite in length; it is impossible for storing and use all the traditional for training. In the existing system of data stream method researcher tackle on the only two issues i.e. concept drift and concept evolution problem of classification. In the existing system for tackling the issue of feature evolution feature set homogeneous technique was developed and also focus on the novel class detection technique for detecting the novel class at a time, but this method required more time for detecting novel and multi novel class detection. Therefore we used the method for detecting the novel class method for data stream classification, we used J48 classification algorithm for detecting the novel class and reducing the time for detecting the novel class. Finally we compared our result with the existing novel class detection method.

References
  1. M.M. Masud, Q. Chen, L. Khan, C. Aggarwal, J. Gao, J. Han and Nikunj C. Oza “ Classification and adaptive novel class detection of Feature-Evolving Data stream” IEEE Trans. Knowledge and Data Eng., vol. 25, no. 7, July 2013.
  2. W. Fan, “Systematic Data Selection to Mine Concept-Drifting Data Streams,” Proc. ACM SIGKDD 10th Int’l Conf. Knowledge Discovery and Data Mining, pp. 128-137, 2004.
  3. Aggarwal, C. C. (2003). A framework for diagnosing changes in evolving data streams. In Proceedings of ACM SIGMOD 2003, pages 575–586.
  4. Babcock, B., Babu, S., Datar, M., Motawani, R., and Widom, J. (2002). Models and issues in data stream systems. In ACM Symposium on Principles of Database Systems (PODS).
  5. J. Gao, W. Fan, and J. Han, “On Appropriate Assumptions to Mine Data Streams,” Proc. IEEE Seventh Int’l Conf. Data Mining (ICDM), pp. 143-152, 2007.
  6. J. Gao, W. Fan, J. Han, and P. Yu. A general framework for mining concept-drifting data streams with skewed distributions. In Proc. SDM’07.
  7. W. Fan, P. S. Yu, and H. Wang. Mining extremely skewed trading anomalies. In Proc. of EDBT’04
  8. F. Korn, S. Muthukrishnan, and Y. Wu. Modeling skew in data streams. In Proc. of SIGMOD ’06
  9. G. Hulten, L. Spencer, and P. Domingos, “Mining Time-Changing Data Streams,” Proc. ACM SIGKDD Seventh Int’l Conf. Knowledge Discovery and Data Mining, pp. 97-106, 2001.
  10. I. Katakis, G. Tsoumakas, and I. Vlahavas, “Dynamic Feature Space and Incremental Feature Selection for the Classification of Textual Data Streams,” Proc. Int’l Workshop Knowledge Discovery from Data Streams (ECML/PKDD), pp. 102-116, 2006.
  11. T. Fawcett. ”in vivo” spam filtering: A challenge problem for data mining. KDD Explorations, 5(2), December 2003
  12. F. Ferrer-Troyano, J. S. Aguilar-Ruiz, and J. C. Riquelme. Incremental rule learning based on example nearness from numerical data streams. In SAC ’05: Proceedings of the 2005 ACM symposium on Applied computing, pages 568–572, New York, NY, USA, 2005. ACM Press.
  13. J. Kolter and M. Maloof, “Using Additive Expert Ensembles to Cope with Concept Drift,” Proc. 22nd Int’l Conf. Machine Learning (ICML), pp. 449-456, 2005.
  14. 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.
  15. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Classification Data Stream Classification J48 classifier novel class features evaluation