Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data.

ABSTRACT: Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat algorithm, and apply them to empower the effects of synthetic minority over-sampling technique (SMOTE) for pre-processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reported in this paper reveal that the performan

SUBMITTER: Li J 

PROVIDER: S-EPMC5533448 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature