Applying Back Propagation Neural Networks in the Prediction of Management Associate Work Retention for Small and Medium Enterprises

Wang, Kuo-Yan; Shun, Hsien-Yu

doi:10.13189/ujm.2016.040501

Cited by 12 publications

(12 citation statements)

References 19 publications

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“…More recent studies have started to use machine learning techniques for HR analytics. Some of them have implemented models that provide interpretable insights (e.g., [19,21,25,38]) and others have implemented non-interpretable models that provide solely the predictions or their ranked scores (e.g., [8,16,28,39]; further literature is detailed in recent surveys, e.g., [18,33]). In the rest of this section, we mainly focus on papers that addressed the pre-hire prediction of recruitment success using ML tools.…”

Section: Methods Dimensionmentioning

confidence: 99%

“…Other recent studies have focused on objective measures of successful recruitment based on employee past performance. Some of these studies examined the post-hire prediction of turnover or performance with predictors collected over the employment period [8,[16][17][18][19][20][21][22][23]. Note that the prediction of turnover or performance using post-hire data (such as absenteeism, punctuality and performance reviews) may be useful as part of some retention activities but may lead to a late discovery of recruitment errors and may often be too late to act upon [8,24].…”

Section: Functional Dimensionmentioning

confidence: 99%

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Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Pessach

Singer

Avrahami

et al. 2020

Decision Support Systems

152

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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.

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Section: Methods Dimensionmentioning

confidence: 99%

Section: Functional Dimensionmentioning

confidence: 99%

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Pessach

Singer

Avrahami

et al. 2020

Decision Support Systems

152

View full text Add to dashboard Cite

show abstract

“…ANN has also been applied in the human resource (HR) field. Wang and Shun [35] applied ANN for predicting the probability of employee retention (staying) in the organisation for a management position. The results indicate that interpersonal skills and working attitude are important success factors for the retention in the management position rather than professional ability factor.…”

Section: Application Of Dm Techniques Based On Annmentioning

confidence: 99%

Determining the best‐fit programmers using Bayes’ theorem and artificial neural network

Prathan

2020

IET softw.

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A data mining-based technique is proposed for the selection and employment of the best-fit programmers to meet the needs of software companies. The proposed technique incorporates Bayes' theorem and artificial neural network (ANN). The datasets used were from two software companies (Company 1 and Company 2) in India, covering the years 2010-2015. Bayes' theorem is used for identifying the prognostic attributes of the best-fit programmers, while the ANN classifier is used for predicting the best-fit programmers. Using a confusion matrix, the ANN classifier performance is 97.2 and 87.3%, 95.8 and 54.5%, and 100 and 75% with regard to accuracy, precision, and recall on the two test datasets of Company 1 and Company 2, respectively. The results show that the technique is effective for predicting the best-fit programmers. Software companies can use this technique in their recruitment and selection process to determine the best-fit employees for the programmer posts. The proposed technique can also be adapted for application in other disciplines such as sports, education, etc., to identify the most suitable person to fill a relevant position.

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“…In addition, the underlying behavior of the data set need to be extracted separately. Next, [ 11 ] applied the back-propagation network in forecasting management associate’s recruitment rates for different enterprises. In their work, the probability of the attributes is computed before being trained and tested by back propagation neural network to check the probability of a recruit to stay in that firm.…”

Section: Introductionmentioning

confidence: 99%

Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

Mohd Jamaludin,

Mohd Kasihmuddin,

Md Ismail

et al. 2020

Entropy

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An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based k satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of k satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia.

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Applying Back Propagation Neural Networks in the Prediction of Management Associate Work Retention for Small and Medium Enterprises

Cited by 12 publications

References 19 publications

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Determining the best‐fit programmers using Bayes’ theorem and artificial neural network

Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

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