Hybrid Segmentation Strategy and Multi-Agent SVMs for Corporate Risk Management in Class Imbalanced Situations

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Abstract This study introduced an emerging architecture with a segmentation strategy for the classification of highly imbalanced datasets. The segmentation strategy was specifically performed by K-means, which divided the majority class into some less imbalanced datasets and yielded more robust training data. Superior forecasting performance of the ensemble mechanism/multi-agent mechanism came with a critical drawback, which was that it lacked interpretability. The study further dealt with the obscure nature of the ensemble mechanism by LEM2 algorithm. The human-readable rules could be taken as a guideline for decision makers to make a suitable judgment in a highly competitive financial environment.