Job Offers Classifier Using Neural Networks and Oversampling Methods

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

Research output: Chapter in Book/Report/Conference proceedingChapter


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. 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.

Original languageEnglish (US)
Title of host publicationStudies in Fuzziness and Soft Computing
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
StatePublished - 2023

Publication series

NameStudies in Fuzziness and Soft Computing
ISSN (Print)1434-9922
ISSN (Electronic)1860-0808

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computational Mathematics

Cite this