Exploring Autoencoder-based Representations for Tabular Data Classification

Abstract

Autoencoders are evaluated as a means of constructing compact and informative vector representations for classification tasks involving high-dimensional tabular data.The methodology addresses the limitations of traditional models that rely on manual feature engineering and taskspecific training.Emphasis is placed on building a generalized look-alike model for targeted advertising, using embeddings derived from subscriber-related entities.The approach is assessed on a real-world telecommunications dataset comprising subscriber demographics, devices, tariffs, and network characteristics.Experimental results demonstrate that embeddings produced by

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