Employability Predictive Model Evaluator Using PART and JRip Classifier

Gatbonton, Twinkle Mae C.; Aguinaldo, Betchie E.

doi:10.1145/3301551.3301569

Cited by 4 publications

(1 citation statement)

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“…A number of works in the labour and employment domain have been published recently, most of which exploring students' and graduates' employment data [15]- [17], [23], or data about employees at organisational level for the purposes of effective HR management [18], [19]. Data mining techniques used include decision trees and Bayesian methods [15]- [19], [24], ensemble methods, MLP, and SVM [16], [25]. The Irish labour data have been explored by Kelly and al.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Techniques for Analysing Employment Data

Nachev¹

2019

IJEAT

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 Abstract: This paper proposes a methodology that uses a large-scale employment dataset in order to explore which factors affect employment and how. The proposed methodology is a combination of predictive modelling, variable significance analysis, and VEC analysis. Modelling is based on logistic regression, linear discriminant analysis, neural network, classification tree, and support vector machine. Following the CRISP-DM standard process model, we train binary classifiers optimising their hyper-parameters and measure their performance by prediction accuracy, ROC analysis, and AUC. Using sensitivity analysis, we rank the variable significance in order to identify and measure factors of employment. Using VEC analysis, we further explore how values of those factors affect employment. Findings show that best performing models are neural networks and support vector machines with preference to the latter for quality of VEC. Experiments also suggest that education and age are primary contributors for correct classification with specific value distribution, discussed in the paper. All results were validated using a rigorous testing procedure that involves training, validation, and test data partitions and a combination of multiple runs along with three-fold cross-validation. This study addresses some gaps in previous research publications, which lack quantification of the conclusions made.We believe, that the proposed methodology provides means for facilitating active employment management and also can be used as a tool for empirical validation of hypotheses and theories in the field.

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