The Efficacy of Autoencoders in the Utilization of Tabular Data for Classification Tasks

Abstract

This article explores the effectiveness of applying auto encoders in the context of classical machine learning tasks, particularly classification tasks on tabular data. Significant emphasis is placed on a detailed comparison of this approach with other widely adopted methods in the field. As an applied task, the creation of a look-alike model for targeted advertising is pursued. Research into the requirements for this model reveals that conventional approaches to processing tabular data and model training cannot yield a solution that qualitatively meets all specified criteria. Evaluation is conducted on a closed dataset provided by a telecommunications operator. This de-identified dataset includes subscriber descriptions, their tariff plans, and mobile devices. The research goal is to assess non-traditional approaches from the domain of deep learning and neural networks in the context of classification tasks on tabular data and compare them with established methods in this context. The research findings demonstrate that the investigated approach yields a solution that surpasses traditional approaches across all specified criteria. Furthermore, the study provides insights into the universality of this approach, indicating its applicability in various domains for tabular data classification tasks. The ability to compress information into vector representations without significant loss and identify new hidden nonlinear dependencies in data enhances the practical value of the approach. Future research will address the formation of multiple autoencoders in different knowledge domains and their integration for specific tasks. Additionally, efforts will focus on identifying the most effective algorithm for finding similar subscribers based on the vector representations obtained from autoencoders.

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