论文标题
Job提供使用神经网络和过度采样方法的分类器
Job Offers Classifier using Neural Networks and Oversampling Methods
论文作者
论文摘要
政策和研究都受益于对个人工作的更好理解。但是，随着越来越多地使用大规模的行政记录来代表劳动力市场活动，因此将有必要对工作进行分类的新自动方法。我们使用从墨西哥最大的工作银行收集的数据集开发了一个自动提供分类器，称为Bumeran https://www.bumeran.com.mx/上次访问：19-01-2022。使用这些算法，我们培训了多级模型来对23个类别之一（不均匀分发）之一进行分类：销售，管理中心，技术，技术，人力资源，物流，市场，营销，健康，美食，融资，秘书，生产，工程，工程，工程，工程，教育，教育，法律，建筑，交流，交流，交流，交流，交流，外交，交流，交流，交流，贸易和分钟。我们使用了Smote，几何效果和Adasyn合成过度采样算法来处理不平衡的类。当应用几何效率算法时，提出的卷积神经网络体系结构将获得最佳结果。
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.
