AEFE: Automatic Embedded Feature Engineering for Categorical Features

• URL: http://arxiv.org/abs/2110.09770v1
• Date: Tue, 19 Oct 2021 07:22:59 GMT
• Title: AEFE: Automatic Embedded Feature Engineering for Categorical Features
• Authors: Zhenyuan Zhong, Jie Yang, Yacong Ma, Shoubin Dong, Jinlong Hu
• Abstract summary: We propose an automatic feature engineering framework for representing categorical features, which consists of various components including custom paradigm feature construction and multiple feature selection. Experiments conducted on some typical e-commerce datasets indicate that our method outperforms the classical machine learning models and state-of-the-art deep learning models.
• Score: 4.310748698480341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The challenge of solving data mining problems in e-commerce applications such as recommendation system (RS) and click-through rate (CTR) prediction is how to make inferences by constructing combinatorial features from a large number of categorical features while preserving the interpretability of the method. In this paper, we propose Automatic Embedded Feature Engineering(AEFE), an automatic feature engineering framework for representing categorical features, which consists of various components including custom paradigm feature construction and multiple feature selection. By selecting the potential field pairs intelligently and generating a series of interpretable combinatorial features, our framework can provide a set of unseen generated features for enhancing model performance and then assist data analysts in discovering the feature importance for particular data mining tasks. Furthermore, AEFE is distributed implemented by task-parallelism, data sampling, and searching schema based on Matrix Factorization field combination, to optimize the performance and enhance the efficiency and scalability of the framework. Experiments conducted on some typical e-commerce datasets indicate that our method outperforms the classical machine learning models and state-of-the-art deep learning models.

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