Data mining classification technique for talent management using SVM

Yasodha, S.; Prakash, P.S.

doi:10.1109/icceet.2012.6203768

Cited by 14 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…During the searches in the Scopus database, other papers with themes related to the use of analytics in the context of HR could be found, such as e-HRM (electronic Human Resources Management, related to IT support in the construction and implementation of IS prepared for the HR demands, according to Schalk et al, 2013), Artificial Intelligence and Big Data in specific HR practices and subsystems (such as talent management and admissions, for example), according to Hamilton & Sodeman (2020), Pillai & Sivathanu (2020), Vaidya et al (2020), Garcia-Arroyo & Osca (2019), Oentaryo et al (2018), Brynjolfsson and Mitchell (2017), Aral et al (2012), Yasodha & Prakash (2012) and Jantan et al (2009). Such applications are sometimes addressed without the use of typical keywords such as "Human Resources Analytics" or "People Analytics" and may delineate a field derived from the use of technology in the context of HRM, but perhaps not central to HR Analytics, as suggest Cheng & Hackett (2021).…”

Section: Collection Of Papersmentioning

confidence: 99%

Management Implications for HR Analytics in Light of Systems Theory

Peres,

Riccio,

Laurindo

2023

Br. J. Ed., Tech. Soc.

View full text Add to dashboard Cite

Resumo. Desde os anos 2000, o campo de HR Analytics experimenta constante crescimento de trabalhos publicados sob focos variados, mas visando a ampliação do valor da Gestão de Recursos Humanos. Recentemente, a literatura tem se concentrado nos fatores a serem considerados nos frameworks de HR Analytics, sugerindo a questão de "como" HR Analytics deve ser praticado e orientado por objetivos; ao contrário das abordagens iniciais (ainda abundantes) de "o que" deve ser feito. Este trabalho visa abordar lacunas que auxiliem na definição dos recursos de gestão, pesquisando nuances nos objetivos de HR Analytics que impliquem em formas distintas de gestão da atividade. Duas abordagens principais foram combinadas: (i) uma análise quantitativa e qualitativa de publicações recentes e (ii) uma abordagem sob os construtos da Teoria de Sistemas. Definições, abordagens, temas subjacentes, áreas de estudo relacionadas e lacunas acadêmicas foram analisadas a partir de 231 publicações na base de dados Scopus até 2021. A análise destacou características de interesse, cujo agrupamento levou ao desenho de distintos (mas relacionados) objetivos e formas de gerenciar HR Analytics. Além disso, comparações com a criação de conhecimento de atividades correlatas permitem a proposição de uma taxonomia como direcionadora de objetivos e uma agenda de pesquisa.

show abstract

Section: Collection Of Papersmentioning

confidence: 99%

Management Implications for HR Analytics in Light of Systems Theory

Peres,

Riccio,

Laurindo

2023

Br. J. Ed., Tech. Soc.

View full text Add to dashboard Cite

show abstract

“…S. Yashoda and P.S. Prakash [12] discussed about optimization of classification models by the application of support vector machines. Youzheng Chang and Guan Ming [13] in their paper explained that the application of data mining techniques have been extended to processes detecting associations between personal profile and work behavior and analyzing employee data for many other tasks like staffing, budgeting, training etc.…”

Section: Related Workmentioning

confidence: 99%

An application of data mining to improve personnel performance evaluation in higher education sector in India

Sharma

Goyal

2015

2015 International Conference on Advances in Computer Engineering and Applications

View full text Add to dashboard Cite

Data mining is a process of extracting knowledge from large databases. Knowledge is appreciated as ultimate power now a days and considered as very important factor for the success of any organization because it has impacted the role of people working in that organization. In last decade, the higher education sector in India has grown radically. Thus HR management has implemented various improvements and it is hard for organizations to retain a motivated and competent workforce. Clever investment in human resource and its effective management has become very necessary today. HRM in higher education sector is further complicated by qualitative and multidimensional definition of "performance" of an academician (employee) in this sector. This paper aims to propose a model for predicting employee performance in higher education institutes with the help of classification and prediction techniques of data mining. The objective of model is to facilitate decisions in a challenging and emerging field of HRM in higher education sector.

show abstract

“…Las máquinas de vectores de soporte se pueden equiparar a los análisis estadísticos de regresión lineal o no lineal comúnmente utilizados, como la regresión múltiple, pero son mucho más robustos, la calidad de las máquinas de soporte vectorial se determina con el entrenamiento y la capacidad de generalización (Cristianini et al,2000). Yasodha et al, (2012), tomaron factores tales como la experiencia humana, el conocimiento y la capacidad de juicio y aplicaron técnicas de minería de datos utilizando un método híbrido entre coeficiente de contingencia de atributo de clase (CACC) y máquinas de soporte vectorial (SVM) para la clasificación de datos para la gestión y selección de recursos humanos para determinados puestos laborales.…”

Section: Introductionunclassified

Máquinas de Soporte Vectorial, Clasificador Naïve Bayes y Algoritmos Genéticos para la Predicción de Riesgos Psicosociales en Docentes de Colegios Públicos Colombianos

2018

View full text Add to dashboard Cite

ResumenEn este artículo se presenta una nueva metodología basada en la aplicación de algoritmos Máquina de Soporte Vectorial, Naïve Bayes y Algoritmos Genéticos en diagnósticos de evaluaciones psicosociales para la identificación del grado de riesgo psicosocial en docentes de colegios públicos en Colombia. Se realizó un estudio comparativo del modelo de aprendizaje de máquina para la predicción: Máquinas de Soporte Vectorial (SVM) y Naïve Bayes, en dos etapas, primero con todas las variables y segundo, reduciendo la dimensionalidad de los datos aplicando: algoritmos genéticos. Se seleccionaron las cuarenta mejores variables con mejor eficiencia en la precisión de la predicción. La base de datos utilizada se componía de 3000 registros epidemiológicos, que correspondían a docentes de colegios públicos del área metropolitana de una ciudad colombiana. El uso de SVM reconoce fácilmente variables de tipo fisiológico y el mejor desempeño de predicción se obtuvo con una eficiencia en la precisión del 96,3%. Palabras clave: aprendizaje de máquina; máquinas de soporte vectorial; Naïve Bayes; algoritmos genéticosAbstract This paper presents a new methodology based on the application of Support Vector Machine algorithms, Naïve Bayes and Genetic Algorithms in diagnostics of psychosocial evaluations for the identification and prediction of the psychosocial risk level of public-school teachers in Colombia. A comparative study of the model of machine learning for prediction was carried out: Support Vector Machines (SVM) and Naïve Bayes, in two stages, first with all the variables and second, reducing the dimensionality of the database applying genetic algorithms, The best forty variables with the best efficiency in prediction accuracy were selected. The database used consisted of 3000 epidemiological records, which corresponded to teachers from public schools in the metropolitan area of a Colombian city. The use of SVM easily detected variables of physiological type and the best prediction performance was obtained with accuracy of 96.3%.

show abstract

Data mining classification technique for talent management using SVM

Cited by 14 publications

References 6 publications

Management Implications for HR Analytics in Light of Systems Theory

Management Implications for HR Analytics in Light of Systems Theory

An application of data mining to improve personnel performance evaluation in higher education sector in India

Máquinas de Soporte Vectorial, Clasificador Naïve Bayes y Algoritmos Genéticos para la Predicción de Riesgos Psicosociales en Docentes de Colegios Públicos Colombianos

Contact Info

Product

Resources

About