現如今，投資者需要通過進行財務困境預測來決定投資哪些公司，以防止損失。現有研究考慮了處理不同類別的集合，例如使用堆疊集成方法將財務比率（FRs）分為長期（LT）和短期（ST）屬性，並且另一項研究使用堆疊方法結合了Beneish M-score等額外特徵來改進。在這些研究中，長期特徵中存在某些特定的灰色區域，難以區分困境和非困境，而這可以通過使用Beneish等額外特徵來幫助預測。利用串行組合模型可以潛在地實現現有研究尚未探索的灰色區域。在本研究中，使用了一種最先進的串行組合模型，其中每個基學習器都實現了不同的特徵集。此外，串行組合中的閾值是使用一種廣泛使用的優化算法，即遺傳算法自適應優化的。使用362家台灣公司的數據，這種新穎模型可以達到與堆疊集成分類器基準相當的結果，同時提供選定的閾值，使得解釋性得以進一步探索額外特徵。結果顯示了具有競爭力的誤分類成本和公司影響分析，推薦了合適的架構。.

Nowadays, investors need to decide which companies to invest in by performing financial distress predictions to prevent loss. Existing studies have considered treating distinct sets of categories, such as splitting the financial ratios (FRs) into long-term (LT) and short-term (ST) attributes using a stacking ensemble approach, and another study incorporated an additional set of features such as Beneish M-score using stacking for improvement. From these studies, there exists some specific gray area from LT features that is difficult to distinguish between distress and nondistress, which can be helped using additional features such as Beneish to predict. Utilising serial combination is potentially able to implement the existence of the gray area which existing study has not explored. In this study, a state-of-the-art serial combination model is used where each base-learner is implemented with distinct sets of features. In addition, the thresholds in the serial combination are optimized adaptively using a widely-used optimization algorithm which is the genetic algorithm. Using 362 Taiwan companies data, the novel model can achieve results as good as the stacking ensemble classifier as baseline while providing selected thresholds which allow interpretability to explore further additional features. The results have been provided with competitive misclassification costs and companies impact analysis to recommend the suitable architecture.

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