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Reseach Article

Instance-based vs Batch-based Incremental Learning Approach for Students Classification

by Roshani Ade, P. R. Deshmukh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 3
Year of Publication: 2014
Authors: Roshani Ade, P. R. Deshmukh
10.5120/18504-9580

Roshani Ade, P. R. Deshmukh . Instance-based vs Batch-based Incremental Learning Approach for Students Classification. International Journal of Computer Applications. 106, 3 ( November 2014), 37-41. DOI=10.5120/18504-9580

@article{ 10.5120/18504-9580,
author = { Roshani Ade, P. R. Deshmukh },
title = { Instance-based vs Batch-based Incremental Learning Approach for Students Classification },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 3 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number3/18504-9580/ },
doi = { 10.5120/18504-9580 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:38:27.778208+05:30
%A Roshani Ade
%A P. R. Deshmukh
%T Instance-based vs Batch-based Incremental Learning Approach for Students Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 3
%P 37-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is difficult to find the hidden information in the educational database system, because of the rapid increase of the student's data. The hidden information from the educational databases can be used for the various predictions like students' performance, offering different career choices to students, prediction of student's enrollment into various courses and many more. This data can be learned incrementally by using instance based or batch based approach. The instance based method is just like an online learning, the system will handle each instance incrementally, the algorithm itself is an updatable, and the knowledge will be updated by every instance in time. In the batch based approach, instances are coming in the batches and will be operated in a bulk, so the processing time requires for it is less as compared to instance based approach and learning new concept is possible when the data is available in a batches. The paper proposes three approaches of incremental learning and compares for handling students data and compares the results of the same.

References
  1. Robi Polikar, Lalita Udpa, Satish S. Udpa, Vasant Honavar, "Learn++: An incremental learning algorithm for supervised neural networks", IEEE Transactions on systems, Man, and Cybernetics-PART C: Applications and Reviews, Vol. 31, No. 4, November 2001.
  2. S. B. Kotsiants, K. Patriacheas, M. Xenos, "A combinational incremental ensemble of classifiers as a technique for predicting students performance in distance education", Knowledge based systems, pp. 529-535, vol. 23, 2010.
  3. N. Oza, "Online ensemble learning," Ph. D. dissertation, Dept. Comput. Sci. , Univ. California, Berkeley, 2001
  4. Seichi Ozawa, Shaoning Pang, and Nikola Kasabov, "Incremental Learning of Chunk Data for Online Pattern Classification Systems", IEEE Transaction on Neural Networks , pp. 1045-9227 , 2008
  5. M. Muhlbaier and R. Polikar, "An Ensemble Approach for Incremental Learning in Nonstationary Environments," Proc. Seventh Int'l Conf. Multiple Classifier Systems, pp. 490-500, 2007.
  6. S. Kotsiants, " An icremental ensemble of classifiers", Artof Intell Rev, pp. 249-266, Vol. 36, 2011.
  7. Kittler, Josef, et al. "On combining classifiers. " IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 226-239, 1998.
  8. N. Littlestone and M. Warmuth, "Weighted majority algorithm," Inform. Comput. , pp. 212–261, vol. 108, 1994.
  9. B. V. Dasarathy and B. V. Sheela, "Composite classifier system design: concept and methodology," proceeding of the IEEE, vol. 67, no. 5, pp. 708-713, 1979.
  10. K. Woods, W. P. J. Kegelmeyer, and K. Bonwyer, "Combination of multiple classifiers using local accuracy estimates,"IEEE Trans. Pattern annal. Mach. Intell. ,vol. 19, no. 4,pp. 405-410,Apr. 1997.
  11. L. I. Kuncheva, "Classifier ensembles for changing environments," in Multiple Classifier Systems, vol. 3077. New York: Springer-Verlag, 2004, pp. 1–15.
  12. L. I. Kuncheva, "Classifier ensembles for detecting concept change in streaming data: Overview and perspectives," in Proc. Eur. Conf. Artif. Intell. , 2008, pp. 5–10.
  13. H. He and S. Chen, "IMORL: Incremental Multiple-Object Recognition and Localization," IEEE Trans. Neural Networks, vol. 19, no. 10, pp. 1727-1738, Oct. 2008
  14. Yang Hai, college of of Science, Minzu university of China, Wei He, College of Science, Minzu university of China, " Incremental learing algorithm for SVM based on voting principle", International journal of Information Processing and Management, Vol. 2, Number 2, April 2011.
  15. Haibo He, Yuan Cao, "SSC: A Classsifier combinaion method based on signal strength", IEEE Transactions on Neural Networks and Learning systems, Vol 23, No. 7, July 2012.
  16. Jesse Read, Albert Bifet, Bernhard Pfahringer, Geoff Holmes, "Batch Incremental versus Instance-incremental learning in Dynamic and evolving data,
  17. Y. Freund and R. E. Schapire, "Decision-theoretic gneralization of online learning and an appliation to boosting," Journal of Computer and Systme Sciences, vol. 55, no. 1, pp. 119-139, 1997.
  18. R. R. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, "Boosting the Margin: A new explanation for the effectivenesss of voting methods," Annals of Statistics, vol. 26, no. 5, pp. 1651-1686, 1998.
  19. N. C. Oza, "AveBoost2:Boosting for Noisy Data, "5th Int. Workshop on multiple classifier systems, in Lecture Notes in Computer Science, vol. 3077, pp. 31-40. F. Roli, J. Kittler and T. Windeatt, Eds. Springer, 2004.
  20. StijnViaene, Richard A. Derrig, and Guido Dedene, "A Case Study of Applying BoostingNaive Bayes to Claim Fraud Diagnosis" ,Actions On Knowledge and Data Engineering, Vol. 16, No. 5, May 2004, 612-620.
  21. BojanMihaljevic,Pedro Larrañaga, Concha Bielza, "Augmented Semi-naive Bayes Classifier" ,IEEE Transactions on Systems, Man and Cypernetics-PartB: Cybernetics, Vol. 36, No. 5, Oct 2006, 1149-116.
  22. V. Robles,P. Larrañaga, J. M. Pria, E. Menasalvas, M. S. Perez, " Interval Estimation Naïve Bayes",Advanced Data Mining and Applications", Vol 4632, 2007, pp 134-145
  23. Liangxiao Jiang,Dianhong Wang, ZhihuaCai, Xuesong Yan, "Survey of Improving Naive Bayes for Classification",Advances in Artificial Intelligence,Vol 8109, 2013, pp 159-167
  24. Roshani Ade, Dr. P. R. Deshmukh, "Classification of students using psychometric tests with the help of incremental naïve baiyes algorithm, IJCA, Vol. 89, No. 14, pp. 27-31, March 2014.
  25. D. Aha and D. Kibler, "Instance-based learning algorithms. Machine Learning", vol. 6, pp. 37–66, 1991
  26. Martin Brent, "Instance based learning:Nearest neighbor with generalization",Hamilton, New Zealand, 1995.
  27. Sylvain Roy, "Nearest Neighbour with Generalization" , Christchurch, New Zealand.
  28. Cleary, John G. , and Leonard E. Trigg, "K*: An instance based learner using an entropic distance measure. " ICML,pp. 108-114, 1995.
  29. C. Giraud-Carrier, "A Note on the Utility of Incremental Learning," Artificial Intelligence Comm. , vol. 13, no. 4, pp. 215-223, 2000. Roshani Ade, Dr. P. R. Deshmukh, "An incremental ensemble of classifiers as a technique for prediction of students career choice", "IEEE conference on network and soft computing, pp 427-430,aug 2014.
  30. S. Lange and S. Zilles, "Formal Models of Incremental Learning and Their Analysis," Proc. Int'l Joint Conf. Neural Networks, vol. 4, pp. 2691-2696, 2003.
  31. H. He and S. Chen, "IMORL: Incremental multiple-object recognition and localization," IEEE Trans. Neural Netw. , vol. 19, no. 10, pp. 1727–1738, Oct. 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Incremental learning education system ensemble voting scheme.