本論文的研究目標是建構一個由多標記複合成的最佳疾病診斷工具，所考慮的評估準則為操作者特徵曲線在特定特異度範圍之線下面積(pAUC)。在常態分布假設下，我們推導多標記線性組合之pAUC以及最佳線性組合之必要條件。由於函數本身過於複雜使得計算困難。除此之外，我們也發現其最佳解可能不唯一，以及局部極值存在，這些情況使得現有演算法的運用受限，我們因此提出多重初始值演算法。當母體參數未知時，我們利用最大概似估計量以獲得樣本pAUC以及令其極大化之最佳線性組合，並證明樣本最佳線性組合將一致性地收斂到母體最佳線性組合。在進一步的研究中，我們針對單標記的邊際判別能力、多標記的複合判別能力以及個別標記的條件判別能力，分別提出相關統計檢定方法。這些統計檢定被運用至兩個標記選取的方法，分別是前進選擇法與後退淘汰法。我們運用這些方法以選取與疾病檢測有顯著相關的標記。本論文透過模擬研究來驗證所提出的演算法、統計檢定方法以及標記選取的方法。另外，也將這些方法運用在數組實際資料上。 / The aim of this work is to construct a composite diagnostic tool based on multiple biomarkers under the criterion of the partial area under a ROC curve (pAUC) for a predetermined specificity range. Recently several studies are interested in the optimal linear combination maximizing the whole area under a ROC curve (AUC). In this study, we focus on finding the optimal linear combination by a direct maximization of the pAUC under normal assumption. In order to find an analytic solution, the first derivative of the pAUC is derived. The form is so complicated, that a further validation on the Hessian matrix is difficult. In addition, we find that the pAUC maximizer may not be unique and sometimes, local maximizers exist. As a result, the existing algorithms, which depend on the initial-point, are inadequate to serve our needs. We propose a new algorithm by adopting several initial points at one time. In addition, when the population parameters are unknown and only a random sample data set is available, the maximizer of the sample version of the pAUC is shown to be a strong consistent estimator of its theoretical counterpart. We further focus on determining whether a biomarker set, or one specific biomarker has a significant contribution to the disease diagnosis. We propose three statistical tests for the identification of the discriminatory power. The proposed tests are applied to biomarker selection for reducing the variable number in advanced analysis. Numerical studies are performed to validate the proposed algorithm and the proposed statistical procedures.

增長層級式自我組織映射網路(GHSOM)屬於一種非監督式類神經網路，為自我組織映射網路(SOM)的延伸，擅長於對樣本分群，以輔助分析樣本族群裡的共同特徵，並且可以透過族群間存在的空間關係假設來建立分類器，進而辨別出異常的資料。 因此本研究提出一個創新的對偶方法(即為一個建立決策支援系統架構的方法)分別對舞弊與非舞弊樣本分群，首先兩類別之群組會被配對，即辨識某一特定無弊群體的非舞弊群體對照組，針對這些配對族群，套用基於不同空間假設所設立的分類規則以檢測舞弊與非舞弊群體中是否有存在某種程度的空間關係，此外並對於舞弊樣本的分群結果加入特徵萃取機制。分類績效最好的分類規則會被用來偵測受測樣本是否有舞弊的嫌疑，萃取機制的結果則會用來標示有舞弊嫌疑之受測樣本的舞弊行為特徵以及相關的輸入變數，以做為後續的決策輔助。 更明確地說，本研究分別透過非舞弊樣本與舞弊樣本建立一個非舞弊GHSOM樹以及舞弊GHSOM樹，且針對每一對GHSOM群組建立分類規則，其相應的非舞弊/舞弊為中心規則會適應性地依循決策者的風險偏好最佳化調整規則界線，整體而言較優的規則會被決定為分類規則。非舞弊為中心的規則象徵絕大多數的舞弊樣本傾向分布於非舞弊樣本的周圍，而舞弊為中心的規則象徵絕大多數的非舞弊樣本傾向分布於舞弊樣本的周圍。 此外本研究加入了一個特徵萃取機制來發掘舞弊樣本分群結果中各群組之樣本資料的共同特質，其包含輸入變數的特徵以及舞弊行為模式，這些資訊將能輔助決策者(如資本提供者)評估受測樣本的誠實性，輔助決策者從分析結果裡做出更進一步的分析來達到審慎的信用決策。 本研究將所提出的方法套用至財報舞弊領域(屬於財務舞弊偵測的子領域)進行實證，實驗結果證實樣本之間存在特定的空間關係，且相較於其他方法如SVM、SOM+LDA和GHSOM+LDA皆具有更佳的分類績效。因此顯示本研究所提出的機制可輔助驗證財務相關數據的可靠性。此外，根據SOM的特質，即任何受測樣本歸類到某特定族群時，該族群訓練樣本的舞弊行為特徵將可以代表此受測樣本的特徵推論。這樣的原則可以用來協助判斷受測樣本的可靠性，並可供持續累積成一個舞弊知識庫，做為進一步分析以及制定相關信用決策的參考。本研究所提出之基於對偶方法的決策支援系統架構可以被套用到其他使用財務數據為資料來源的財務舞弊偵測情境中，作為輔助決策的基礎。 / The Growing Hierarchical Self-Organizing Map (GHSOM) is extended from the Self-Organizing Map (SOM). The GHSOM’s unsupervised learning nature such as the adaptive group size as well as the hierarchy structure renders its availability to discover the statistical salient features from the clustered groups, and could be used to set up a classifier for distinguishing abnormal data from regular ones based on spatial relationships between them. Therefore, this study utilizes the advantage of the GHSOM and pioneers a novel dual approach (i.e., a proposal of a DSS architecture) with two GHSOMs, which starts from identifying the counterparts within the clustered groups. Then, the classification rules are formed based on a certain spatial hypothesis, and a feature extraction mechanism is applied to extract features from the fraud clustered groups. The dominant classification rule is adapted to identify suspected samples, and the results of feature extraction mechanism are used to pinpoint their relevant input variables and potential fraud activities for further decision aid. Specifically, for the financial fraud detection (FFD) domain, a non-fraud (fraud) GHSOM tree is constructed via clustering the non-fraud (fraud) samples, and a non-fraud-central (fraud-central) rule is then tuned via inputting all the training samples to determine the optimal discrimination boundary within each leaf node of the non-fraud (fraud) GHSOM tree. The optimization renders an adjustable and effective rule for classifying fraud and non-fraud samples. Following the implementation of the DSS architecture based on the proposed dual approach, the decision makers can objectively set their weightings of type I and type II errors. The classification rule that dominates another is adopted for analyzing samples. The dominance of the non-fraud-central rule leads to an implication that most of fraud samples cluster around the non-fraud counterpart, meanwhile the dominance of fraud-central rule leads to an implication that most of non-fraud samples cluster around the fraud counterpart. Besides, a feature extraction mechanism is developed to uncover the regularity of input variables and fraud categories based on the training samples of each leaf node of a fraud GHSOM tree. The feature extraction mechanism involves extracting the variable features and fraud patterns to explore the characteristics of fraud samples within the same leaf node. Thus can help decision makers such as the capital providers evaluate the integrity of the investigated samples, and facilitate further analysis to reach prudent credit decisions. The experimental results of detecting fraudulent financial reporting (FFR), a sub-field of FFD, confirm the spatial relationship among fraud and non-fraud samples. The outcomes given by the implemented DSS architecture based on the proposed dual approach have better classification performance than the SVM, SOM+LDA, GHSOM+LDA, SOM, BPNN and DT methods, and therefore show its applicability to evaluate the reliability of the financial numbers based decisions. Besides, following the SOM theories, the extracted relevant input variables and the fraud categories from the GHSOM are applicable to all samples classified into the same leaf nodes. This principle makes that the extracted pre-warning signal can be applied to assess the reliability of the investigated samples and to form a knowledge base for further analysis to reach a prudent decision. The DSS architecture based on the proposed dual approach could be applied to other FFD scenarios that rely on financial numbers as a basis for decision making.

現今解釋錯誤偵測系統及前扣帶皮質（ACC）的關係之理論主要為增強學習理論。增強學習理論認為個體會在行為後對於行為結果產生預期，並將該預期與實際結果進行比較，若實際結果較預期結果差則會活化ACC進而觀察到較大的FN（Feedback Negativity）振幅。近年來有學者提出奠基於增強學習理論的錯誤可能性理論，錯誤可能性理論則認為當個體在學習到行為與結果之間的關聯後，當接收到可能犯錯的訊息時便會活化ACC而引起較大的FN。本研究主要的目的為探討增強學習理論及錯誤可能性理論的適用性，其次為探討風險之因素是否能反映於FN上。由於兩理論對於風險情境中是否會觀察到FN有不同的預測，錯誤可能性理論預測會在高風險的情況下觀察到較大的FN；而增強學習理論則預測由於風險畫面並非回饋畫面，故風險不會影響FN。實驗一藉由探討風險與FN間之關係企圖提供兩理論初步之區分並提供風險研究的實驗證據，實驗一結果顯示FN確實會反映風險之因素。也點出增強學習理論用以解釋錯誤偵測系統之不完備之處。而實驗二則利用操弄回饋結果好壞及高低錯誤可能性以檢驗錯誤可能性對於錯誤偵測系統之必要性，實驗二結果顯示錯誤可能性為事件評估之因素之一。除此之外，實驗二亦提供FN反映懊悔之支持性證據。

根據Koster、De Lissnyder、Derakshan及De Raedt（2011）的注意力轉移困難假說，憂鬱者因注意力控制能力受損，而難以從負向訊息中轉移注意力，進而引發反芻，並再度強化憂鬱症狀。雖然反芻可再細分為深思反省與苦惱自責式反芻，但Koster等人未探究注意力轉移困難對苦惱自責式反芻的影響。此外，過往注意力訓練研究作業眾多且效果不一，又偏重改善個體注意力投入以減緩憂鬱。然而，卻鮮少探討注意力訓練對注意力轉移困難的介入，能否改善個體的苦惱自責式反芻程度。因此，本研究將同時探討注意力訓練能否改變高苦惱自責式反芻者對負向訊息的注意力偏誤（含注意力投入與轉移困難），進而降低苦惱自責式反芻程度。本研究以點偵測作業為注意力訓練作業，將高、低苦惱自責式反芻者隨機分派至注意力訓練組或注意力訓練控制組，接受為期兩週、共四次的注意力訓練，並於前、後測階段注意力測量作業中，檢驗對負向訊息的注意力偏誤與三階段中苦惱自責式反芻程度。本研究結果發現高苦惱自責式反芻者對負向訊息無明顯注意力偏誤，注意力訓練作業對高苦惱自責式反芻者的注意力歷程未有明顯影響，乃至苦惱自責式反芻程度的時序變化與接受注意力訓練與否無明顯關聯。本研究結果不支持原先假設、注意力困難假說及過去研究結果。然而，過往學者多強調個體高度負向認知與憂鬱情緒對注意力偏誤的影響，故本研究事後同時納入憂鬱與苦惱自責式反芻程度，欲探討憂鬱苦惱自責式反芻者對負向訊息有無注意力偏誤，乃至注意力訓練對憂鬱苦惱自責式反芻者注意力偏誤的訓練效果。本研究結果僅發現在修正版Posner作業中，憂鬱苦惱自責式反芻者更容易將注意力從負向訊息中轉移開來；在點偵測作業中，未有組間效果；在注意力訓練中，未有訓練效果。最後，本研究將於討論中，探討研究結果的可能原因，並提出本研究限制與未來研究上的建議。 / According to the impaired disengagement hypothesis (Koster, De Lissnyder, Derakshan, & De Raedt, 2011), the dysphoric that are difficult to disengage from negative stimuli due to low attentional control tend to ruminate, and then even worsen their depressive symptoms. Actually, rumination can be differentiated into two components: reflective pondering and brooding, but the core tenet of impaired disengagement hypothesis only puts the emphasis on rumination rather than brooding. Besides, there are many studies investigating the attention-training effects on depressive symptoms rather than rumination. To date, no studies even have investigated the training effects on impaired disengagement and brooding. Therefore, we aimed to examine the effects of attention training on attention bias toward negative stimuli, impaired disengagement from negative stimuli and brooding level in brooders. We investigated the training effect in brooding and non-psychiatric control participants via dot-probe task. During a two-week period, all of the participants were randomly assigned to complete 4 sessions of either attention training or no training. Also, participants completed two attentional tasks examined attention bias at baseline and post-training, and self-reported questionnaires of brooding and depressive symptoms at baseline, post-training, and follow-up. Overall, results indicate that brooders didn’t show attention bias to negative stimuli. Also, no beneficial effects of attention training on attention bias and brooding level were found in brooders. The previous hypothesis, impaired disengagement hypothesis and studies in the past were not supported. However, many cognitive models of depression have postulated that individuals with high levels of negative cognition and depressive affect tend to maintain their attention toward negative information. Therefore, we took levels of depression and brooding into account, and aimed to examine the effects of attention training on both attention bias toward negative stimuli and impaired disengagement from negative stimuli in depressive brooders. Results indicate that depressive brooders tend to disengage from negative stimuli in modified Posner task. No other findings in dot-probe task and attention-training task. Implications of these findings in depressive brooders are discussed and directions for future research are advanced.

空載光達系統於建置國土測繪基本資料扮演關鍵角色，依國土測繪法，為確保測繪成果品質，應依測量計畫目的及作業精度需求辦理儀器校正。國土測繪中心已於102年度建置航遙測感應器系統校正作業中，提出矩形建物之平屋頂面做為空載光達系統校正之可行性，而其所稱之校正，是以點雲精度評估待校件空載光達系統所得最終成果品質，並不對儀器做任何參數改正，但其校正成果可能因不同人員操作而有差異，因此本研究嘗試建立一套空載光達點雲半自動化精度評估程序，此外探討以山形屋脊線執行點雲精度評估之可行性。 由於光達點雲為離散的三維資訊，不論是以山形屋脊線或矩形建物之平屋頂面作為標物執行點雲精度評估，均須先萃取屋頂面上之點，為避免萃取成果受雜訊影響，本研究引入粗差偵測理論，發展最小一乘法結合李德仁以後驗變方估計原理導出的選擇權迭代法(李德仁法)將非屋頂點視為粗差排除。研究中分別對矩形建物之平屋頂面及山形屋脊線進行模擬及真實資料實驗，其中山形屋脊線作為點雲精度評估之可行性實驗中發現不適合用於評估點雲精度，因此後續實驗僅以萃取矩形建物之平屋頂面點雲過程探討粗差比率對半自動化點雲精度評估程序之影響。模擬實驗成果顯示最小一乘法有助於提升李德仁法偵測粗差數量5%至10%；真實資料實驗，以含有牆面點雲的狀況為例，則有助提升5%的偵測粗差數量。本研究由逐步測試結果提出能夠適用於真實狀況的半自動化之點雲精度評估程序，即使由不同人員操作，仍能獲得一致的成果，顯示本研究半自動化精度評估程序之可信度。 / The airborne LiDAR system plays a crucial role in building land surveying data. Based on the Land Surveying and Mapping Act, to ensure the quality of surveying, instrument calibration is required. The approach proposed by National Land Surveying and Mapping Center (NLSC) in 2013 was confirmed the feasibility for airborne LiDAR system calibration using rectangular horizontal roof plane. The calibration mean to assess the final quality of airborne LiDAR system based on the assessment of the accuracy of the point cloud, and do not adjust the instrument. But the results may vary according to different operators. This study attempts to establish a semi-automatic procedure for the accuracy assessment of point clouds from airborne LiDAR system. In addition, the gable roof ridge lines is discussed for its feasibility for the accuracy assessment of point cloud. No matter that calibration is performed using rectangular horizontal roof plane or gable roof ridge line, point clouds located on roof planes need to be extracted at first. Therefore, Least Absolute Deviation (LAD) combined with the Iteration using Selected Weights (Deren Li method) is developed to exclude the non-roof points which regarded as gross errors and eliminate their influences. The simulated test and actual data test found that gable roof ridge lines are not suitable for accuracy assessment. As for the simulated test using horizontal roof planes, LAD combined with Deren Li method prompts the rate of gross error detection about 5% to 10% than that only by Deren Li method. In actual test, data contains wall points, LAD combined with Deren Li method can prompt about 5%. Meanwhile, a semi-automatic procedure for real operations is proposed by the step-by-step test. Even different operators employ this semi-automatic procedure, consistent results will be obtained and the reliability can achieve.