財務報表舞弊不僅對國際金融市場造成嚴重影響，並傷害審計人員之聲譽，但大多數的審計人員缺乏足夠經驗可偵測出財務報表舞弊之事件，故本研究提出一混合模型，其可分為四大部分：特徵擷取技術、急速學習機、績效評估知識管理，以及整體學習概念。其中整體學習概念可用來克服傳統單一模型之缺陷，亦可有效提升模型預測效果，並可降低審計人員所面臨的審計失敗風險。此外，本研究亦進一步將急速學習機內部之黑盒決策機制，轉化為易於使決策者瞭解之知識，決策者（審計人員）可採用此模型對有限的審計資源進行最佳分配，亦可提升審計品質與效果，並做成較佳之決策。
Fraudulent financial reports (FFR) have caused serious turbulence in financial markets and have damaged auditors' reputations. While auditors are the last line of defence in detecting FFR, many auditors lack the sufficient experience to deal with the related tasks. This investigation introduces hybridized model (HM) which incorporates four main parts: feature selection ensemble, extreme learning machine (ELM) ensemble, performance evaluation by multiple criteria decision making (MCDM), and knowledge generation, to alleviate auditing risks. The feature selection ensemble is grounded on ensemble learning. The advantage of this ensemble combination is its ability to catch errors made by an individual technique. ELM with superior generalization ability was adopted as the basic classifier for the HM. The study further decomposed the ELM's inherent structure to yield comprehensible rules in logical statement. Furthermore, the knowledge visualized process is supported by real example, can assist auditors who must allocate limited resources to make reliable decisions.
