Fuzzy Scorecards, FHOWA, and a New Fuzzy Similarity Based Ranking Method for Selection of Human Resources

Luukka, Pasi; Collan, Mikael

doi:10.1109/smc.2013.108

Cited by 4 publications

(8 citation statements)

References 12 publications

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“…The available literature regarding the use of fuzzy logic in a such a context is particularly rich. There are papers, such as [12] and [2], that propose the use of a fuzzy system for the selection of applicants to a specific position in an institution. On a similar field, the work of [13] presents a strategy for recommending human resources for project leaders using adaptative fuzzy logic.…”

Section: A Bibliographic Review and Related Workmentioning

confidence: 99%

“…Human resource management (HRM) is defined as the process that organize, manage and leads a team [1]. Always an important process, the HRM has gone through a major transformation in form and function in the past 3 decades [2]. In nowadays, such a competence is considered a source of sustained competitive advantage for organizations operating in a global economy [3].…”

Section: Introductionmentioning

confidence: 99%

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A Process for Human Resource Performance Evaluation Using Computational Intelligence: An Approach Using a Combination of Rule-Based Classifiers and Supervised Learning Algorithms

Goes

Oliveira

2020

IEEE Access

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This paper proposes a process for human resource performance evaluation using computational intelligence techniques. The human resource (or employee's) performance evaluation is essentially a regular assessment and review of an employee's performance on the job. This evaluation can be performed in different ways, depending on the kind of job of the employee and on the company's politics or business area. The process proposed on this research combines Fuzzy logic, text sentiment analysis and supervised learning classification techniques, such as a multi layer perceptron artificial neural network, decision tree algorithms and naïve bayes into ensemble classifiers, in an attempt to provide a fair evaluation process, minimizing or even eliminating common problems caused by simple objective or subjective approaches. The data provided for this research was originated from several evaluations applied in two Brazilians institutions. Simulation results shows consistence on the data generated by this proposed process, indicating a good perspective for applications on companies of most business areas.

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Section: A Bibliographic Review and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Process for Human Resource Performance Evaluation Using Computational Intelligence: An Approach Using a Combination of Rule-Based Classifiers and Supervised Learning Algorithms

Goes

Oliveira

2020

IEEE Access

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“…Scholars were invited to provide articles related to their research interests in Amini et al [11]. In other studies the input of outside decision makers was solicited to assess candidates according to their own area of expertise [86,101]. Another technique was to ask users to evaluate an item's relevance with respect to their own preferences [48,151].…”

Section: Collect Datamentioning

confidence: 99%

“…Other systems included self-ascribed keywords [95] and self-provided documentation [95,126]. Another technique employed multiple decision-makers to score candidates according to the criteria within their area of specialty [86,101]. Despite its benefits, extracting knowledge items from different sources illuminates inconsistency and heterogeneity [126].…”

Section: Collect Datamentioning

confidence: 99%

“…Specific state-of-the-art literature pertaining to each use case [42] 2013 assign reviewers to conference papers hybrid concept, prob. dist., topic modeling optimization Li and Watanabe [93] 2013 assign reviewers to conference papers not defined multi-criteria, not defined, manual optimization Luukka and Collan's [101] 2013 select job candidates explicit multi-criteria, linguistic or fuzzy, hetero. sources aggregation-fuzzy Liu et al [97] 2014 assign reviewers to conference papers implicit multi-criteria, prob.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge aggregation in people recommender systems : matching skills to tasks

Nguyen¹

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People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives. Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessària. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives.

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Strategic R&D Project Analysis: Keeping It Simple and Smart

Collan

Luukka

2015

Fuzzy Technology

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Fuzzy Scorecards, FHOWA, and a New Fuzzy Similarity Based Ranking Method for Selection of Human Resources

Cited by 4 publications

References 12 publications

A Process for Human Resource Performance Evaluation Using Computational Intelligence: An Approach Using a Combination of Rule-Based Classifiers and Supervised Learning Algorithms

A Process for Human Resource Performance Evaluation Using Computational Intelligence: An Approach Using a Combination of Rule-Based Classifiers and Supervised Learning Algorithms

Knowledge aggregation in people recommender systems : matching skills to tasks

Strategic R&D Project Analysis: Keeping It Simple and Smart

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