Classifying the Category of Workers using Crowd sourced Job Seekers Data
AS. Rajathilagam3, K.Kavitha3
1S.Rajathilagam, Research Scholar, Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India.
2Dr.K.Kavitha, Assistant Professor, Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India.
Manuscript received on October 16, 2019. | Revised Manuscript received on 25 October, 2019. | Manuscript published on November 10, 2019. | PP: 2389-2394 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4238119119/2019©BEIESP | DOI: 10.35940/ijitee.A4238.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Crowdsourcing refers to decomposing complex jobs to multiple tasks and solve those task with multiple workers through open call in distributed networking environment. The recruitment of employees for organization has undergone transformation from traditional method to digital domain. Online recruitment facilitates just-in-time hiring to requesters and enables the workers to compete in the global market. This paper proposed an Efficient Machine Learning Crowdsourced(EMLC) method for E-recruitment which uses Crowdsourcing method to collect resumes from the workers and details of work from the requesters. The data is collected from a private job agency through an online recruitment portal which consist of recruiters from companies and job seekers based on qualifications and experience related to their field. The data collected from recruitment portal is analyzed with Machine Learning Approach with decision tree algorithms like ID3, CART and C4.5 for better selection of efficient person to complete the job. Various performance metrics such as Accuracy, Error rate, Recall etc were used to the Crowdsourced Database to categorize the job seekers efficiently. The proposed method gives better result for online recruiting through Crowdsourcing.
Keywords: Crowdsourcing, Online Recruitment, Machine Learning, Decision Tree.
Scope of the Article: Machine Learning