FeatGeNN: Improving Model Performance for Tabular Data with
  Correlation-based Feature Extraction
Sammuel Ramos Silva Rodrigo Silva
Automated Feature Engineering (AutoFE) has become an important task for any
machine learning project, as it can help improve model performance and gain
more information for statistical analysis. However, most current approaches for
AutoFE rely on manual feature creation or use methods that can generate a large
number of features, which can be computationally intensive and lead to
overfitting. To address these challenges, we propose a novel convolutional
method called FeatGeNN that extracts and creates new features using correlation
as a pooling function. Unlike traditional pooling functions like max-pooling,
correlation-based pooling considers the linear relationship between the
features in the data matrix, making it more suitable for tabular data. We
evaluate our method on various benchmark datasets and demonstrate that FeatGeNN
outperforms existing AutoFE approaches regarding model performance. Our results
suggest that correlation-based pooling can be a promising alternative to
max-pooling for AutoFE in tabular data applications.