The week's pick
Random Articles
Reseach Article
A Probabilistic Machine Learning Approach for Eligible Candidate Selection
| 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
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
- Yu Lou, Ran Ren and Yiyang Zhao; Stanford University; “A Machine Learning Approach for Future Career Planning”
- http://cs229.stanford.edu/proj2010/LouRenZhang-AMachineLearningApproachForFutureCareerPlanning.pdf ; Access Date: 30th April, 2016.
- 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).
- https://en.wikipedia.org/wiki/Bayes%27_theorem; Access Date: 8th June, 2016.
- [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.
- William Feller, “An Introduction to Probability Theory and Its Applications”, Volume 1, 3rd Edition, ISBN: 978-0-471-25708-0, January 1968.
- 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.
- 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.
- Mike Oaksford, Birkbeck College, London; Nick Chater, University College London; “Bayesian Rationality: The Probabilistic Approach to Human Reasoning”
- Haun Liu, Rudy Setiono; National University of Singapore; “A probabilistic approach to feature selection-a filter solution”, ICML, 1996 – Citeseer
- 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.
- 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
- 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
- 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
- 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
- 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
Keywords