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arXiv · 2207.06223

Job Offers Classifier using Neural Networks and Oversampling Methods

Germán Ortiz·Gemma Bel Enguix·Helena Gómez-Adorno·Iqra Ameer·Grigori Sidorov
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Abstract

Both policy and research benefit from a better understanding of individuals' jobs. However, as large-scale administrative records are increasingly employed to represent labor market activity, new automatic methods to classify jobs will become necessary. We developed an automatic job offers classifier using a dataset collected from the largest job bank of Mexico known as Bumeran https://www.bumeran.com.mx/ Last visited: 19-01-2022.. We applied machine learning algorithms such as Support Vector Machines, Naive-Bayes, Logistic Regression, Random Forest, and deep learning Long-Short Term Memory (LSTM). Using these algorithms, we trained multi-class models to classify job offers in one of the 23 classes (not uniformly distributed): Sales, Administration, Call Center, Technology, Trades, Human Resources, Logistics, Marketing, Health, Gastronomy, Financing, Secretary, Production, Engineering, Education, Design, Legal, Construction, Insurance, Communication, Management, Foreign Trade, and Mining. We used the SMOTE, Geometric-SMOTE, and ADASYN synthetic oversampling algorithms to handle imbalanced classes. The proposed convolutional neural network architecture achieved the best results when applied the Geometric-SMOTE algorithm.

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Germán Ortiz, Gemma Bel Enguix, Helena Gómez-Adorno, Iqra Ameer, Grigori Sidorov. 2022-07-03. Job Offers Classifier using Neural Networks and Oversampling Methods. https://arxiv.org/abs/2207.06223

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