A 3-in-1 framework for human resources' selection and positioning based on machine learning tools
by Panagiota Pampouktsi; Katia-Lida Kermanidis; Markos Avlonitis
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 13, No. 4, 2021

Abstract: Administration is aiming to control the performance of human resources in order to achieve its best possible exploitation and through it resources economy that is an important issue for all organisations. Innovations about human resource evaluation systems, is a success factor of all administrative changes. Management of human resources is based on proper selection and positioning of the staffs that add value to an organisation. Artificial intelligence is the new ally for managers, but it is not wide-spread in the public sector. In this study, we collected and assessed personnel's data in a public organisation and by using various classification algorithms, specific models were built according to job description and employees' qualifications. Our scope is the development of an innovative framework, using machine learning techniques for meritocratic personnel selection for recruitment and simultaneously, positioning either horizontally in the departments or vertically in leadership positions, unifying three procedures in one.

Online publication date: Fri, 07-Jan-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com