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A Probabilistic Machine Learning Approach for Eligible Candidate Selection

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Marium-E-Jannat, Sayma Sultana Chowdhury, Munira Akther

Marium-E-Jannat, Sayma Sultana Chowdhury and Munira Akther. A Probabilistic Machine Learning Approach for Eligible Candidate Selection. International Journal of Computer Applications 144(10):1-4, June 2016. BibTeX

	author = {Marium-E-Jannat and Sayma Sultana Chowdhury and Munira Akther},
	title = {A Probabilistic Machine Learning Approach for Eligible Candidate Selection},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {144},
	number = {10},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {1-4},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2016910439},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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Machine Learning, Probability, Statistics, Naive Bayes Classifier.