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

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Probabilistic Machine Learning Approach for Eligible Candidate Selection

by Marium-E-Jannat, Sayma Sultana Chowdhury, Munira Akther
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 144 - Number 10
Year of Publication: 2016
Authors: Marium-E-Jannat, Sayma Sultana Chowdhury, Munira Akther
10.5120/ijca2016910439

Marium-E-Jannat, Sayma Sultana Chowdhury, Munira Akther . A Probabilistic Machine Learning Approach for Eligible Candidate Selection. International Journal of Computer Applications. 144, 10 ( Jun 2016), 1-4. DOI=10.5120/ijca2016910439

@article{ 10.5120/ijca2016910439,
author = { Marium-E-Jannat, Sayma Sultana Chowdhury, Munira Akther },
title = { A Probabilistic Machine Learning Approach for Eligible Candidate Selection },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 144 },
number = { 10 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume144/number10/25212-2016910439/ },
doi = { 10.5120/ijca2016910439 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:47:14.850450+05:30
%A Marium-E-Jannat
%A Sayma Sultana Chowdhury
%A Munira Akther
%T A Probabilistic Machine Learning Approach for Eligible Candidate Selection
%J International Journal of Computer Applications
%@ 0975-8887
%V 144
%N 10
%P 1-4
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now-a-days Machine learning approach is used to solve many problems where intelligence is involved. Lots of time consuming task are done by computers with the power of statistics. In this paper, a machine learning based candidate selection procedure is proposed and implemented for a particular field. A huge amount of activity is involved in the job recruitment procedure. To reduce the manual task a probabilistic machine learning approach is described in this paper. A popular machine learning approach named Naive Bayes Classifier is used to implement the method. Baseline criteria selection depends on the recruiters demand. The proposed system learns from training dataset and produces a short listed eligible list based on learning. The more perfectly one feed the system result will be more accurate.

References
  1. Yu Lou, Ran Ren and Yiyang Zhao; Stanford University; “A Machine Learning Approach for Future Career Planning”
  2. http://cs229.stanford.edu/proj2010/LouRenZhang-AMachineLearningApproachForFutureCareerPlanning.pdf ; Access Date: 30th April, 2016.
  3. Dunja Mladenic and Marko Grobelnik; J. Stefan University; “Feature selection for unbalanced class distribution and Naive Bayes”; Proceedings of the 16th International Conference on Machine Learning (ICML).
  4. https://en.wikipedia.org/wiki/Bayes%27_theorem; Access Date: 8th June, 2016.
  5. [Gedikli et al. 2011] RF-Rec: Fast and accurate computation of recommendations based on rating frequencies, Proceedings of the 13th IEEE Conference on Commerce and Enterprise Computing - CEC 2011, Luxembourg, 2011.
  6. William Feller, “An Introduction to Probability Theory and Its Applications”, Volume 1, 3rd Edition, ISBN: 978-0-471-25708-0, January 1968.
  7. John O’Donovan & Barry Smyth; “Trust in Recommender Systems”; Proceedings of the 10th international conference on Intelligent user interfaces; Pages 167-174, ACM, New York, NY, USA; 2005; ISBN:1-58113-894-6.
  8. J. Ben Schafer, Joseph Konstan, John Riedl ; “Recommender Systems in E-Commerce”; Proceedings of the 1st ACM conference on Electronic commerce Pages 158-166, ACM, New York, NY, USA, 1999, ISBN:1-58113-176-3.
  9. Mike Oaksford, Birkbeck College, London; Nick Chater, University College London; “Bayesian Rationality: The Probabilistic Approach to Human Reasoning”
  10. Haun Liu, Rudy Setiono; National University of Singapore; “A probabilistic approach to feature selection-a filter solution”, ICML, 1996 – Citeseer
  11. S.B. Kotsiantis, University of Peloponnese, Greece; “Supervised Machine Learning: A Review of Classification Techniques”; Emerging Artificial Intelligence Applications in Computer Engineering, IOS Press, 2007.
  12. Joachim Herbst, “A Machhine Learning Approach to Workflow Management”; 11th European Conference on Machine Learning Barcelona, Catalonia, Spain, May 31 – June 2, 2000; Online ISBN: 978-3-540-45164-8
  13. Nava Tintarev, Judith Masthoff; University of Aberdeen, Scotland, U.K; “A Survey of Explanations in Recommender Systems”; Data Engineering Workshop, 2007 IEEE 23rd International Conference on; E-ISBN: 978-1-4244-0832-0; Pages: 1-810
  14. G. Adomavicius ; Carlson Sch. of Manage., Minnesota Univ., Minneapolis, MN, USA ; A. Tuzhilin;Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions Published in: IEEE Transactions on Knowledge and Data Engineering  (Volume:17 ,  Issue: 6 , Pages: 734-749); ISSN: 1041-4347
  15. Linyuan Lü, Matúš Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, Tao Zhou; “Recommender Systems”; Physics Reports, Volume: 519 Issue: 1, October,2012 Pages: 1-49, Elsevier
  16. Paul Resnick, AT&T, Labs-Research, Murray Hill, N.J.; Hal R. Varian, Dean of the School of Information Management and Systems, University of California, Berkeley; “Recommender Systems”; Communications of the ACM, Volume 40 Issue 3, March 1997, Pages 56-58, ACM, New York, NY, USA.
Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Probability Statistics Naive Bayes Classifier.

Powered by PhDFocusTM