文章基本信息

标题：Data Mining for Prediction of Human Performance Capability in the Software Industry
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作者：Gaurav Singh Thakur ; Anubhav Gupta ; Sangita Gupta 等
期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2015
卷号：5
期号：2
页码：53
DOI：10.5121/ijdkp.2015.5205
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The recruitment of new personnel is one of the most essential business processes which affect the quality ofhuman capital within any company. It is highly essential for the companies to ensure the recruitment ofright talent to maintain a competitive edge over the others in the market. However IT companies often facea problem while recruiting new people for their ongoing projects due to lack of a proper framework thatdefines a criteria for the selection process. In this paper we aim to develop a framework that would allowany project manager to take the right decision for selecting new talent by correlating performanceparameters with the other domain-specific attributes of the candidates. Also, another important motivationbehind this project is to check the validity of the selection procedure often followed by various bigcompanies in both public and private sectors which focus only on academic scores, GPA/grades of studentsfrom colleges and other academic backgrounds. We test if such a decision will produce optimal results inthe industry or is there a need for change that offers a more holistic approach to recruitment of new talentin the software companies. The scope of this work extends beyond the IT domain and a similar procedurecan be adopted to develop a recruitment framework in other fields as well. Data-mining techniques provideuseful information from the historical projects depending on which the hiring-manager can make decisionsfor recruiting high-quality workforce. This study aims to bridge this hiatus by developing a data-miningframework based on an ensemble-learning technique to refocus on the criteria for personnel selection. Theresults from this research clearly demonstrated that there is a need to refocus on the selection-criteria forquality objectives.