Job Offers Classifier Using Neural Networks and Oversampling Methods
Por:
Ortiz G., Enguix G.B., Gómez-Adorno H., Ameer I., Sidorov G.
Publicada:
1 ene 2023
Resumen:
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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Filiaciones:
Ortiz G.:
Posgrado en Ciencia e Ingeniería de la Computación Universidad Nacional Autónoma de México, Mexico City, Mexico
Enguix G.B.:
Instituto de Ingeniería, Universidad Nacional Autónoma de México, Mexico City, Mexico
Gómez-Adorno H.:
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mexico City, Mexico
Ameer I.:
Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico
Sidorov G.:
Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico
|