Multi-Class Imbalanced Data Oversampling For Vertebral Column Pathologies Classification

Medical data mining problems are usually characterized by examples of some of the classes appearing more frequently. Such a learning difficulty is known as imbalanced classification problems. This contribution analyzes the application of algorithms for tackling multi-class imbalanced classification in the field of vertebral column diseases classification. Particularly, we study the effectiveness of applying a recent approach, known as Selective Oversampling for Multi-class Imbalanced Datasets (SOMCID), which is based on analyzing the structure of the classes to detect those examples in minority classes that are more interesting to oversample. Even though SOMCID has been previously applied to data belonging to different domains, its suitability in the difficult vertebral column medical data has not been analyzed until now. The results obtained show that the application of SOMCID for the detection of pathologies in the vertebral column may lead to a significant improvement over state-of-the-art approaches that do not consider the importance of the types of examples.