CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

Classification of Students using Psychometric Tests with the help of Incremental Naive Bayes Algorithm

by Roshani Ade, P. R. Deshmukh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 14
Year of Publication: 2014
Authors: Roshani Ade, P. R. Deshmukh
10.5120/15701-4624

Roshani Ade, P. R. Deshmukh . Classification of Students using Psychometric Tests with the help of Incremental Naive Bayes Algorithm. International Journal of Computer Applications. 89, 14 ( March 2014), 26-31. DOI=10.5120/15701-4624

@article{ 10.5120/15701-4624,
author = { Roshani Ade, P. R. Deshmukh },
title = { Classification of Students using Psychometric Tests with the help of Incremental Naive Bayes Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 14 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number14/15701-4624/ },
doi = { 10.5120/15701-4624 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:09:15.785518+05:30
%A Roshani Ade
%A P. R. Deshmukh
%T Classification of Students using Psychometric Tests with the help of Incremental Naive Bayes Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 14
%P 26-31
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this study, we validate that the incremental leaning as a technique in data mining can be used to classify the students according to their interest by conducting some aptitude test including psychometric tests on students. So that the students can get the correct carrier choice, student can learn the subject in which he/she is interested and improve their as well as institutes performance in terms of result. Recent years have observed very increasing interest in the topic of incremental learning, as it is having the ability to learn from new data introduces with the system even after the classifier has been produced from the formerly available data. It is required that the leaning should be done without accessing previously learned data and must remember previously acquired knowledge. This can be achieved by using incremental naïve bayes classifier.

References
  1. Firat Hardlac, "Classification of educational backgrounds of students using musical intelligence and perception with the help of genetic neural networks", Expert systesm with applications vol. 36, 2009, pp. 6708-6713.
  2. N. M. Norwawai, S. F. Abdusalam, C. F. Hibadulla, B. M. Shuaibu, "Classsification of students performance in computer programming course according to learning style", 2nd conference on data mining and optimization, 27-28 Oct. 2009, Selangor, Malaysia.
  3. S. Kotsiantis, K. Patriarcheas, M. Xenos, "A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education", Knowledge-Based Systems vol 23, 2010 pp. 529–535.
  4. Remco R. Bouckaert, "Naive Bayes Classifiers That Perform Well with Continuous Variables", Advances in Artificial Intelligence, Volume 2871, 2003, pp 326-333.
  5. Han-joon Kim, Jae-young Chang, "Improving Naïve Bayes Text Classifier with Modified EM Algorithm",Advances in Intelligent Data Analysis , Vol 2810, 2003, pp 143-154.
  6. StijnViaene, Richard A. Derrig, and Guido Dedene, "A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis" ,Actions On Knowledge and Data Engineering, Vol. 16, No. 5, May 2004, 612-620.
  7. 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.
  8. 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
  9. Liangxiao Jiang,Dianhong Wang, ZhihuaCai, Xuesong Yan, "Survey of Improving Naive Bayes for Classification",Advances in Artificial Intelligence,Vol 8109, 2013, pp 159-167
  10. R. R. Ade, Dr. P. R. Deshmukh, "Methods for Incremetnal Learning: A Survey", International Journal of Data Mining & Knowledge Management Proc. , IJDKP), 03(04), 119 - 125 July 2013.
  11. Seiichi Ozawa, Shaoning Pang and Nikola Kasabov, Incremental Learning of Chunk Data for Online Pattern Classification Systems, IEEE Transactions on Neural Networks, 2008,1045-9227.
  12. Sheng Uei Guan and Fangming Zhu, An Incremental Approach to Genetic Algorithms Based Classification, IEEE Transactions on Systems Man. And Cybernetics, Vol. 35, No. 2, 2005, 1083-4419.
  13. J. R. Millan, "Rapid, safe, and incremental learning of navigation strategies," IEEE Trans. Syst. , Man, Cybern. , Part B: Cybern. , vol. 26, no. 3, pp. 408–420, Jun. 1996.
  14. G. Y. Chen and W. H. Tsai, "An incremental-learning-by-navigation approach to vision-based autonomous land vehicle guidance in indoor environments using vertical line information and multiweighted generalized Hough transform technique," IEEE Trans. Syst. , Man, Cybern, Part B: Cybern. , Vol. 28, no. 5, pp. 740–748, Oct. 1998.
  15. Sheng Wan, Larry E. Banta, Parameter Incremental Learning Algorithm for Neural Networks, IEEE Transactions on Neural Networks, Vol. 17, No. 6, 2006, 1045-9227.
  16. Ryan Elwell, RobiPolikar, Incremental Learning of Concept Drift in Non-stationary Environments, IEEE Trans. On NN, Vol 22, No. 10, Oct. 2011.
  17. David Martinez-Rego, Beatriz Perez-Sanchez, Oscar Fontenla-Romero, Amparo Alonso-Betanzos, A robust incremental learning method for non-stationary environments, Neurocomputing 74, 2011.
  18. Sciichi Ozawa, Soon Lee Toh, and Shigeo Abe, Incremental Learning for Online Face Recognition", Proceedings of International Joint Conference on NN, Montreal, Canada, July 31-August 4,2005.
  19. HaitaoZhaYuo and Pong Chi Yuen, Incremental Linear Discriminant Analysis for Face Recognition", IEEE Trans. On Systems, MAN and Cyber. , vol. 38, No. 1, Feb,. 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Incremental Learning Naïve Bayes Algorithm