An automated approach for binary classification on imbalanced data

Abstract Imbalanced data is present in various business areas and must be dealt with the appropriate resampling techniques and classification algorithms. However, there is a magnitude of multiple combinations of resampling and learning methods to handle imbalanced data that require specialised knowledge to be used correctly. In this paper, several approaches, ranging from more accessible and more advanced in the domains of data resampling and cost-sensitive techniques, will be considered to handle imbalanced data. The application developed delivers recommendations of the most suited combinations of techniques for a specific dataset, by extracting and comparing dataset meta-features values recorded in a knowledge base. It facilitates effortless classification and automates part of the machine learning pipeline with comparable or better results to a state-of-the-art solution and with a much smaller execution time.