Constructing classifiers for imbalanced data using diversity optimisation

Abstract

Imbalanced data is challenging in classification. This paper proposes a new approach to address imbalanced data by adopting diversity optimisation to generate synthetic instances for over-sampling the minority class. Diversity optimisation assures that the generated instances are close to the minority group but not identical. It also ensures the optimal spread of the generated instances in the space. We develop two formulations named as Diversity-based Average Distance Over-sampling (DADO) and Diversity-based Instance-Wise Over-sampling (DIWO). We evaluate the proposed formulations’ performance by designing experiments using both synthetic and real data with unbalanced classes. We examine th..