The development of an expert system for effective selection and appointment of the jobs applicants in human resource management

Mehrabad, Mohammad Saidi; Brojeny, Mohammad Fathian

doi:10.1016/j.cie.2007.06.023

Cited by 66 publications

(34 citation statements)

References 2 publications

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“…[3] When all objects have been assigned, recalculate the positions of the K centroids. [4] Repeat Steps 2 and 3 until the centroids no longer move.…”

Section: K-means Clusteringmentioning

confidence: 99%

“…They are organizational criteria, functional/departmental criteria and individual job criteria [11]. Finally, the recruitment committee must consider the adaptation of the job, departmental and organizational characteristics to the applicant's characteristics [3], [4]. Hence the recruitment committee designs each level of the recruitment process to reflect their needs.…”

Section: Selection Processmentioning

confidence: 99%

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Applicability of Clustering and Classification Algorithms for Recruitment Data Mining

Sivaram¹,

Ramar²

2010

IJCA

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Recruitment of appropriate employees and their retention are the major concerns towards creating the competitive strength in the knowledge economy. Every year IT companies recruit fresh graduates through their campus selection programs after examining their skills by conducting tests, group discussion and a number of interviews. The recruitment process requires enormous amount of effort and investment. During each phase of the recruitment process, candidates are filtered based on some performance criteria. Intense analysis on the system indicates that a pattern exists among the candidates selected for an industry. The problem domain is complex and the aspects of candidates that impact the recruitment process is not explicit. In this research, the domain knowledge is extracted through knowledge acquisition techniques. Data mining techniques that fit the problem are determined. A study has been made by applying K-means and fuzzy C-means clustering and decision tree classification algorithms to the recruitment data of an industry. Experiments were conducted with the data collected from an IT industry to support their hiring decisions. Pruned and unpruned trees were constructed using ID3, C4.5 and CART algorithms. From the comparative study, it has been observed that the clustering algorithms are not much suitable for the problem and performance of the C4.5 decision tree algorithm is high. Using the constructed decision trees, discussions were made with the domain experts to deduce viable decision rules.

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“…[3] When all objects have been assigned, recalculate the positions of the K centroids. [4] Repeat Steps 2 and 3 until the centroids no longer move.…”

Section: K-means Clusteringmentioning

confidence: 99%

Section: Selection Processmentioning

confidence: 99%

Applicability of Clustering and Classification Algorithms for Recruitment Data Mining

Sivaram¹,

Ramar²

2010

IJCA

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“…Expert system/ Knowledge-based system (Hooper et al, 1998) and (Mehrabad & Brojeny, 2007) Data Mining (M. J. Huang et al, 2006) and (Chien & Chen, 2008) Artificial Neural Network (L. C. Huang et al, 2004)and (M. J.…”

Section: Personnel Selectionmentioning

confidence: 99%

Intelligent Techniques for Decision Support System in Human Resource Management

Jantan¹,

Hamdan²,

Othman³

2010

Decision Support Systems Advances In

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“…In recent years, regarding the ever-growing advances in information technology, many studies have emphasized application of decision support systems and expert systems as assistance to encounter the challenge [3][4][5]. Chien and Chen (2008) [6] developed 30 rules as employment strategies on the basis of the decision tree and relational rules.…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy ELECTRE Based Multiple Criteria Method for Personnel Selection

Jasemi

Ahmadi

2017

Scientia Iranica

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Abstract. In today's competitive environment, quali ed human resources are considered as one of the major keys to the organizations' success. So, an e cient solution to the problem of personnel selection is more necessary than ever. Besides many studies in the literature of the eld, this paper presents a novel fuzzy ELECTRE approach which is categorized as a Multiple-Criteria Decision Making (MCDM) technique. In this approach, the weights and ranks are determined by linguistic variables while both quantitative and qualitative criteria are considered simultaneously. At last, the implementation of the model is illustrated and the results are compared with those of TOPSIS.

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The development of an expert system for effective selection and appointment of the jobs applicants in human resource management

Cited by 66 publications

References 2 publications

Applicability of Clustering and Classification Algorithms for Recruitment Data Mining

Applicability of Clustering and Classification Algorithms for Recruitment Data Mining

Intelligent Techniques for Decision Support System in Human Resource Management

A New Fuzzy ELECTRE Based Multiple Criteria Method for Personnel Selection

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