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10.1016/j.dss.2020.113290 http://scihub22266oqcxt.onion/10.1016/j.dss.2020.113290 C7252110!7252110!32501316     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32501316&cmd=llinks): Failed to open stream: HTTP request failed! 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Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming |pdf=|usr=}}{{32501316}} gab.com Text Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming JO: Decis Support Syst http://www.kidney.de/pget.php?&tw=1&pmid=32501316 #FOAvip In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods.We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two. Twit Text FOAVip Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming ????Decis Support Syst http://www.kidney.de/pget.php?&tw=1&pmid=32501316 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32501316 #FOAvip English Wikipedia <ref>{{Cite pmid|32501316}}</ref> Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming #MMPMID32501316 Pessach D; Singer G; Avrahami D; Chalutz Ben-Gal H; Shmueli E; Ben-Gal I Decis Support Syst 2020[Jul]; 134 (ä): 113290 PMID32501316show ga In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods.We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two. äEmployees recruitment: A prescriptive analytics approach via machine l(epmc).pdf DeepDyve Pubget Overpricing