Preprocessing compensation techniques for improved classification of imbalanced medical datasets
Agnieszka Wosiak, Sylwia Karbowiak · Annals of Computer Science and Information Systems · 2017
The paper describes the study on the problem of applying classification techniques in medical datasets with a class imbalance.The aim of the research is to identify factors that negatively affect classification results and propose actions that may be taken to improve the performance.To alleviate the impact of uneven and complex class distribution, methods of balancing the datasets are proposed and compared.The experiments were conducted on five datasets -three binary and two multiclass.They comprise several data preprocessing methods applied on data and the classification with different techniques.The study shows that for some datasets there exists a combination of a certain preprocessing method and a classification technique which outperforms other approaches.For datasets with complex distribution or too many features the ratio of correctly predicted labels may be low regardless what resampling method and classification technique has been applied.