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1
Hamilton, Robert. "[Credit] scoring : predicting, understanding and explaining consumer behaviour." Thesis, Loughborough University, 2005. https://dspace.lboro.ac.uk/2134/13053.
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This thesis stems from my research into the broad area of (credit) scoring and the predicting, understanding and explaining of consumer behaviour. This research started at the Univers1ty of Edinburgh on an ESRC funded project in 1988. This work, which is being submitted as the partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough Unvers1ty, consists of an introductory chapter and a selection of papers published 1991 - 2001 (inclusive). The papers address some of the key issues and areas of interest and concern arising from the rapidly evolving and expanding credit (card) market and the highly competitive nature of the credit industry. These features were particularly evident during the late 1980's and throughout the 90's Chapter One provides a general background to the research and outlines some of the key (practical) issues involved in building a (credit) scorecard Additionally, it provides a brief summary of each of the research papers appearing in full in Chapters 2- 9 (inclusive) and ends with some general limitations and conclusions. The research papers appearing in Chapters 2-9 inclusive) are all concerned with predicting, understanding and explaining different types of consumer behaviour in relation to the use of credit cards. For example discriminating between 'GOOD' and 'BAD' repayers of credit card debt on the basis of different definitions of good and bad, the identification of 'slow payers' using different statistical methods; examining the characteristics of credit card users and non-users, and identifying the characteristics of credit card holders most likely to return their credit card.
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Martinez, John Brett. "Credit card credit scoring and risk based lending at XYZ Credit Union." CSUSB ScholarWorks, 2000. https://scholarworks.lib.csusb.edu/etd-project/1752.
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Norrie, James, and not supplied. "Improving results of project portfolio management in the public sector using a balanced strategic scoring model." RMIT University. Property, Construction and Project Management, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070208.152804.
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This thesis suggests improvements, from a strategic perspective, to the practice of scoring projects in public sector organisations. It is argues that current approaches, notably project portfolio managing (PPM), are inadequate for many such organisations, and in fact prone to problems and failure. In particular, present scoring/prioritization approaches in such contexts, largely tend to focus on financial risk/return logic. It is argued that the end result of such a ranking approach is often a non-strategic portfolio project. To address these problems, the candidate proposed the refinement of the scoring approach for project portfolios via the incorporation of Kaplan & Norton's ideas in their Balanced Scorecard (BSC). BSC introduces, apart from purely financial considerations, other 'softer' perspectives (customer, internal business processes, learning and growth) which in combination place a more inclusive emphasis on the vision and strategy of the organisation. In this thesis, it is proposed that the combined PPM and BSC scoring approach amounts to more strategic project selection. Several case studies are conducted to illustrate the merits of the combined PPM/BSC logic. These include case studies in both private and public sector organisations.
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Islam, Md Samsul, Lin Zhou, and Fei Li. "Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk : A Predictive Model For Credit Card Scoring." Thesis, Blekinge Tekniska Högskola, Sektionen för management, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2099.
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Credit Decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. A number of banks use judgmental decisions, means credit analysts go through every application separately and other banks use credit scoring system or combination of both. Credit scoring system uses many types of statistical models. But recently, professionals started looking for alternative algorithms that can provide better accuracy regarding classification. Neural network can be a suitable alternative. It is apparent from the classification outcomes of this study that neural network gives slightly better results than discriminant analysis and logistic regression. It should be noted that it is not possible to draw a general conclusion that neural network holds better predictive ability than logistic regression and discriminant analysis, because this study covers only one dataset. Moreover, it is comprehensible that a “Bad Accepted” generates much higher costs than a “Good Rejected” and neural network acquires less amount of “Bad Accepted” than discriminant analysis and logistic regression. So, neural network achieves less cost of misclassification for the dataset used in this study. Furthermore, in the final section of this study, an optimization algorithm (Genetic Algorithm) is proposed in order to obtain better classification accuracy through the configurations of the neural network architecture. On the contrary, it is vital to note that the success of any predictive model largely depends on the predictor variables that are selected to use as the model inputs. But it is important to consider some points regarding predictor variables selection, for example, some specific variables are prohibited in some countries, variables all together should provide the highest predictive strength and variables may be judged through statistical analysis etc. This study also covers those concepts about input variables selection standards.
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Кузнєцова, Наталія Володимирівна. "Методи і моделі аналізу, оцінювання та прогнозування ризиків у фінансових системах." Doctoral thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/26340.
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Роботу виконано в Інституті прикладного системного аналізу Національного
технічного університету України «Київський політехнічний інститут імені Ігоря Сікорського».У дисертаційній роботі розроблено системну методологію аналізу та оцінювання фінансових ризиків, яка ґрунтується на принципах системного аналізу та менеджменту ризиків, а також запропонованих принципах адаптивного та динамічного менеджменту ризиків. Методологія включає: комбінований метод обробки неповних та втрачених даних, ймовірнісно-статистичний метод оцінювання ризику фінансових втрат, динамічний метод оцінювання ризиків, який передбачає побудову різних типів моделей виживання, метод структурно-параметричної адаптації, застосування скорингової карти до аналізу ризиків фінансових систем і нейро-нечіткий метод доповнення вибірки відхиленими заявками. Містить критерії урахування інформаційного ризику, оцінки якості даних, прогнозів та рішень, квадратичний критерій якості опрацювання ризику та інтегральну характеристику оцінювання ефективності методів менеджменту ризиків. Практична цінність одержаних результатів полягає у створенні розширеної інформаційної технології та інформаційної системи підтримки прийняття рішень на основі запропонованої системної методології.
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LIAO, JEN-CHIEN, and 廖仁傑. "Building model for credit scoring and credit rating of credit cards." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/26485252561720367196.
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博士中原大學
商學博士學位學程
101
The government reinforces risk control for the respect of market mechanism. Bankers consider their costs and profits, and therefore they have to adopt differential interest rates (i.e. determining interest rates based on the level of risks) for both risk control and profits in order to minimize credit risks and maximize excess profits. However, the scoring model must allow the adjustment of credit scoring for different economic environments so that banks are able to play the role of loaning and media of investment financing in an atmosphere of competition and profit seeking. Still, banks are required to publish interest rate information on a regular basis to allow consumers to have choices to get in and out. This study is intended to show how a bank filters out important information value variables and establish credit score cards using a bank as the subject of study. Previous studies focused on building Logit regression models based on finite number of samples. The coefficients used in models and whether bias occur in subsequent statistics tests were rarely discussed in the studies and the assumption of no bias was often made. For this, bootstrapping method was introduced in the study of the credit rating in the bank selected to see if bias was produced in model, thus providing better accuracy for scientific verification model. For the considerations of the static scoring model built for evaluation of new credit card applications and subsequent transactions, dynamic model for behavior scoring (established using quantile regression) and the correlation of client’s probability of default in the economic fluctuation (using copula to evaluate the correlation of probability of default in two years), it is necessary to know whether the models are still applicable, how to convert probability of default into credit scores using linear transform and how to establish internal rating for differential interest rates. Also in order to ensure the stability and reliability of the credit scoring model, the credit rating has to be verified to build a method that meets the capital requirement of Basel Accord. The hope was to establish a credit card scoring system through internal rating in order to reflect clients’ risks, allow reasonable profits and prices that are affordable to consumers and create a win-win for banks and consumers.
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Kuo, Shu-Wei, and 郭淑薇. "A Study on Establishing a Cash-Advance Card Credit Scoring Model and the Card’s Risk Management." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/55290311413014716673.
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碩士國立中央大學
財務金融學系碩士在職專班
94
Abstract Cash-Advance Card is among the typical trendy products brought to the market by many financing businesses here in Taiwan in the last years to answer the needs arise following the change of consumer awareness. Indeed at the beginning stage the sales of cash-advance card creates unexpectedly fruitful margin to these financing businesses however, following the high level NPL (non-performing loan) ratio remaining unchanged as a result of excessive expansion of granting the credit line to the customers under the banks’ competing promotion policy, the financing institutions gradually find it inevitable to expose their shortfall in risk monitoring and controlling systems. In view of these, the present study aims to explore the said issue, through empirical methods, by locating the factors leading to the contract violation for most of the cash-advance card users. The study, by putting variables influencing the contract violation into two aspects: the static aspect, i.e. before-loan personality characteristics, and the dynamic aspect, i.e. after-loan banking credit data, tries to formulate and establish an optimal credit scoring model tailored to the unique cash-advance card products in Taiwan, on the ground of firstly conducting a customer personality characteristics analysis before-loan and secondly implementing a proactive customer credit status management. The study finds the empirical analysis results as the follows: 1. The variables affecting principal risks of cash-advance card customer’s contract violation: In personality characteristics aspect, the present study finds that the major factors affecting credit granting quality and available in the subscribing application form are the four items: income pattern, annual income, education level, application source route. In after-loan banking credit data aspect, the study finds five affecting factors, namely: finalized credit line, initial loan line, batch of short-term loan, credit line multiplication ratio, income contribution percentage. 2. Emphasis on After-loan Proactive Debt Management: This is the most frequently overlooked area in the existing literature according to the literature review, however, empirical approaches demonstrate that after-loan debt management of proactive type is definitely influential to the effectiveness of credit line risk management. The study especially puts focus on checking and verifying the “interim credit line granting” practice found uniquely exclusively in cash-advance card management and finds that there are correlations between customer’s overdue payment and the bank’s finalized credit line, initial loan line, batch of short-term loan, credit line multiplication ratio, and income contribution percentage. 3. Proposal on Continuing Consecutive Credit Risk Management: To the results found through empirical method, the risk management area shall not be confined to the loan seeking customer’s initial conditions or status and shall be well expanded to areas including “continuing consecutive management” and reviewing and modifying the decision making direction timely. The study believes that the financing businesses will not only realize their ideal of sustained operations but also serve as a stream of force contributing to the social stability. 4. Proposal on Further Releasing Credit Line Discretion to Financing Institutions: The study also finds that the governmental authorities and agencies influence the operation performance of financing businesses with their regulations, orders and policies. Fortunately enough the study finds that in recent years the authorities or agencies have been redirecting towards the management models that vest banks larger range of credit line discretion. This will definitely give the banks more room to optimize their continuing consecutive debt management and this, in turn, will add extra supportive forces for risk management domain to develop not only more wholesomely but also more efficiently. Key word: Cash-Advance Card ；Consumer’s Finance；Non-performing loan ratio；Risk Management；Proactive Debt Management
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Liu, Tai-Gu, and 劉泰谷. "The Building and Analysis of Credit Card Scoring Model." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/76501101593995508650.
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碩士世新大學
財務金融學系
92
In a period of tiny profit time, banks can’t make profits like before. In order to keep and increase profits, banks now promote a lot of preferential credit card programs to grab market ratio. But this action could possibly increase credit risk and cost for the banks. In the past, credit card approval was determined by the bank’s employees subjective judgment. But due to the rapid growth of the credit card business, banks now must learn how to use an objective scoring system for card authorization while increasing efficiency and reducing the bad debt ratio. Quite a few credit scoring models have been proposed to forecast a customers’ default rate. But they didn’t take into consideration a rejection inference in the development process, leading to a severse sample selection error, and the prediction power could be thus exaggerated. To correct this bias, this study integrates card holders and the rejected ones to build credit application scoring model, in addition to a credit management scoring model. The former be applied by banks to estimate credit card application’s default rate, and the latter help banks to management the existing quality credit card business.
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Ming-Chien, Lee, and 李明謙. "The Application Of Logistic Regression Model In Credit Card Scoring." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/89074160771372454288.
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碩士輔仁大學
應用統計學研究所
90
Consumer credit nowadays play a major economical role in Taiwan. The volume of credit business has greatly expanded and the use of credit scoring through the evaluation of large credit portfolio becomes crucial to guard against any management risk in the credit industry. The objectives of this study is to devise a credit scoring system for credit granting decisions made by the issue bank of credit card markets. In the process of scoring, individual characteristics profiles are transformed into a score such that the score distributions derived from the two groups: accepted or rejected are separated as much as possible. The score is then a basis for the making decisions about granting credit, adjusting credit limits or targeting specific markets. Scorecards are usually built using the logistic regression method which estimates the relationship between the individual characteristics and the log of the odds (risk) so that the score point weights can be calculate directly from the regression coefficients. Standard exploratory binary analyses : cross-table analysis, association analysis, and chi-square automatic interaction detection (chaid), are performed to detect the significant variables and evaluate the data structure. Sampling design is on the basis of outcome results of the decision tree. It shows that the variables like gender, education level, martial status, job position, occupation, and age related with the response variable : good or bad credit of credit card holders. However, this credit granting decision is not based on the variables such as annual income, etc. We summarize the classification outcomes of logistic regression analysis and compare the performance of models by classification table. And various measures : Kolmogorov-Smirnov two-sample test statistic, divergence statistic, and Gini coefficient are defined and used to describe the relative discriminant power of a scoring system. For comparison purpose, both completely categorical model and mixed model (with both discrete and continuous covariates) are applied. The performance of the mixed model is slightly better than the completely categorical model.
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陳義先. "Combined Logit and ANN Models to Construct the Credit Card Scoring." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/40387013937288078237.
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碩士真理大學
財經研究所
92
In Taiwan, the using amount of credit card and outstanding credit has increased rapidly and the competition of banks turns white-hot. The credit risk also increase. For these reasons, detecting probability of bad debt actively, identifying customers of higher profit return, and increasing customer’s loyalty, is the best direction of bank strategy. This paper applied Logit model and ANN models to construct credit scoring system. Results shows that sex,age,income, education, marriage ,occupation..variables have a significance to normal and default cards. In addition, female, older, high income, high education, marriaged, high title and VIP have a lower credit risk. In the last, we construct a credit scoring systems combined this two models. It will decrease the default risk of credit scoring.
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Liou, Yi-Fan, and 劉一帆. "Prediction and Characterization of Protein Functions Using a Scoring Card Method." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/y89hwe.
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博士國立交通大學
生物資訊及系統生物研究所
104
This study aims to propose a protein analyzing system to predict and characterize proteins using their sequences. This system contains three parts: the protein sampling part, machine learning part and analyzing part. Comparing to the existing protein function predictors, this system is not only used to predict protein functions, but also find the characteristics of proteins. In the data sampling part, the GO terms and SwissProt is used while the machine learning part use the scoring card method due to its easy interpretability. The analysis part contain SCM-PCPI which is used to find the highly correlated physicochemical properties and SCM-VISU which is use to visualize the score distribution on the protein.To evaluate this system, name Scoring Card Inspired Protein Analysis System, SCIPAS, four topics are applied. The first topic is the heme-binding protein (HBP) prediction. The scoring card analyzing results suggest that HBPs have more rigid structures than non-HBPs do. This means HBPs would have stable structures. Since the environment conditions, such as the temperatures and pH, affect the protein structures which decide the protein function, HBP need to keep their structures stable enough to proof denaturation when they pass through different work environments. The second topic is the photosynthetic protein (PSP) prediction. PSPs are responsible for transform the solar energy into the chemical energy that the live organisms can use. PSPs work at the environment containing ROS which are high energy compounds and often attack proteins. After creating the PSP predictor, the scoring card is analyzed using SCM-PCPI. PSPs show to be composed of the amino acids that can neutralize ROS. This would be due to PSPs need to clean the ROS to keep other protein away from the ROS attacking. In the third topic, membrane transporter proteins (MTPs) are studied. After analyzing the physicochemical properties and visualizing the protein using scoring card, the amino acids composed of the channels are not very hydrophilic. This would be caused from MTP folding and keeping the channel transport efficient. In the fourth topic, carbohydrate binding proteins (CBPs) are investigated. In this part, the interlacing patterns are shown and four criteria for CBP engineering are also proposed. Finally, in those topics, the characteristic results have good agreements with previous studies, some characteristics of the proteins are also found.
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Tamara, Vasylenko, and 方恩蘭. "Using Scoring Card Method for Predicting and Characterizing Photosynthetic Proteins from Sequences." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/01987899592335833499.
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碩士國立交通大學
生物資訊及系統生物研究所
103
光合作用是很重要的生物功能，在地球和光合蛋白已經被廣泛地研究了超過五十年。雖然有相當數量的研究文章已發表有關主要光合蛋白複合物（例如光系統I 和光系統II ）的結構和功能，最近越來越多的研究繼續發現參與光合能量轉換新的蛋白質。到現在為止，少於50％阿拉伯芥的蛋白質編碼基因已被實驗確認功能並註釋。在多數研究光合細菌菌株之中，一半左右的藍綠菌的開放閱讀框會編碼假設蛋白。這些資訊和其它的證據顯示，葉綠體的類囊體膜的脈絡仍然包含許多蛋白質，這可能是未知的亞基光合複合物，或那些在維護、修護、運轉及生物合成的整個光合機制發揮輔助作用。關於傳統的方法來研究光合作用蛋白的是相當耗時、昂貴和費力的。因此，理想的有效研究是用計算的方法來開發光合作用蛋白質，可以提供訊息利於進一步的實驗。 目前在辨識一條光合作用蛋白序列的研究，尚無以機器學習工具來專門預測光合蛋白的功能。另一研究方向，葉綠體定位預測大都基於識別N端定位到這些蛋白質特定質體目標序列（cTPs）的開發。這類已開發的蛋白局部預測雖精準度不錯，但其發展是有一些設限。首先，不是所有的葉綠體蛋白質有明顯的cTPs。第二，單細胞光合細菌沒有葉綠體。最後，不是所有的葉綠體蛋白質須從事光合作用如其它程序冗長的（如氨基酸生物合成）發生在這些細胞器。 本研究提出一個嶄新分數評分卡方法(SCMPSP)，只用序列資訊來預測和描述光合作用蛋白。SCMPSP所用的特徵集是雙胜肽組成成分。其所測量的400個雙胜肽傾向分數，是用光合作用蛋白和非光合作用蛋白雙胜肽成分的差異量做為最佳化基因演算法的初始值來更進一步最佳化求得。由雙胜肽傾向分數所推得的20個基本胺基酸的傾向分數，用來發現光合作用蛋白的物化特性。用SCMPSP方法獨立測試的準確度是71.54%，其測所有的測試資料集平均效果達66.17%的準度。最後有四個光合作用蛋白的物化特性被確認：1) 光合作用蛋白嗜好疏水支鏈組成的氨基酸；2) 在細胞膜的環境中，光合作用蛋白易形成α螺旋；3) 光合作用蛋白不易和水有交互作用；4)光合作用蛋白傾向活化電子組成的氨基酸。
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Hsiao, Chiung-Chih, and 蕭瓊治. "Discovering functional peptides of histone lysine methyltransferases using a scoring card method." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/03255267034654677787.
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碩士國立交通大學
生物資訊及系統生物研究所
105
Epigenetics is a popular research topic in recent years, which explores gene expression without modifying DNA sequences. There are two research fields: DNA methylation and histone modifications. The SET domain of the histone lysine methyltransferases (HKMT) modifies lysines of the histone. A lot of studies have verified the important role of motifs in the SET domain. If we can develop an efficient algorithm to discover novel motifs or functional peptides, we can further understand the mechanism of histone lysine methylation. The classical methods that identify functional peptides utilized the sequence alignment methods first and further verified those peptides using biological experiments. In this thesis, we propose a new method, SCMHKMT, based on a scoring card method (SCM) developed in our laboratory to discover novel functional peptides. We collected datasets and calculated the difference between HKMT and non-HKMT sequences in terms of the dipeptide score of SCM. Consequently, we used the SCMHKMT method to screen potential motifs of tripeptides which have high appearance probability and high propensity score. As a result, there were 17 functional tripeptides discovered in which GEE, PNN, WPN, and SCS have been reported in published papers. Using other motif prediction tools, DEE and KGE have also been identified, suggesting that these two peptides are highly potential functional peptides.
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Jung-Chieh, Hsu, and 許榮傑. "The Building of Credit Card Scoring Via Logistic Regression Model Combined with Decision Tree." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/93622354485551178617.
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碩士輔仁大學
應用統計學研究所
96
The objective of this study is to build a credit scoring system to guard against management risk for the credit-issue financial corporations. A pragmatic devise merging the decision tree CHAID techniques with logistic regression is proposed and applied to the model building. This twofold approach uses the classification tree algorithm to generate classifiers from the bank credit card data and the logistic regression model to estimate the relative contribution of predictor variables to the classification rule. It can effectively identify the most predictive subsets and estimate the probability of any credit applicant being a good risk given the characteristics vector.
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Wang, Yin-Jie, and 王音絜. "Discovering a specific secondary structure to identify antioxidant proteins using a scoring card method." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/82658924878612453220.
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碩士國立交通大學
分子醫學與生物工程研究所
105
Antioxidant protein (AOP) is considered as biological endogenous antioxidant defense mechanisms, tends to donate electrons to scavenge Reactive oxygen species (ROS). The issue in the past has been widely disused which mostly focus on one topic of AOP, but rarely on overall. Therefore, this study proposes a novel methodological approach called SCMAOP to estimating the propensity scores of 400 dipeptides and 20 amino acids in order to design prediction methods and characterize AOPs based on a scoring card method (SCM). Moreover, using SCMAOP to finding the functional recognition pattern on antioxidant protein. The SCMAOP method for predicting AOPs achieves a test accuracy of 86.17%. A dataset consisting of known AOPs corresponding its ROS substrate both H2O2 and O22- were collected from PeroxiBase and SwissProt database. Additionally, informative physicochemical properties of 20 amino acids are identified using the estimated propensity scores to characterize AOPs as follows: 1) AOP have high propensity derived from alpha helix designed sequences. 2) The structure of AOP have a lot of beta-turn structure which may as catalytic environments. 3) The specific functional structure of AOP may located from beta-strand connecting loop-helix conformation which contain beta-turn structure. Further, we develop a tool called IDAOP based on the specific functional structure to verify our finding. The analysis results reveals that the specific secondary structure is important to antioxidant protein, beta-turn structure is the main impact factor, identifying 131 putative AOP from putative dataset after IDAOP method. According to the published studied that four putative AOP from top 50 rankings may have similar antioxidant function. Therefore, we believe that combine SCMAOP and IDAOP can help biologists to finding novel antioxidant protein.
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Chen, Yi-pyng, and 陳怡萍. "The Credit and Idleness Scoring Models of Cash Card under Consideration of Reject Inference." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/25969281374545082795.
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碩士國立高雄第一科技大學
風險管理與保險所
93
Abstract The traditional credit and idleness scoring models are usually parameterised using a sample of accepted applicants only, but not to consider about the rejected ones. However, this may lead to biased estimates of the parameters. Therefore, in this paper, we derive the reject inference of cash card users from the Heckman’s two-stage method with all applicants that are either accepted or rejected. Moreover, in order to realize the predictive performance of the credit and idleness scoring models adjusted by Heckman’s two-stage method, we design two logistic models with different samples for comparison. Results show that there is sample selection bias by credit modeling, but no sample selection bias by idleness modeling. We also find that the predictive performance of Heckman two-stage model is better by credit scoring model than by idleness scoring model.
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Lai, Shih-Chung, and 賴仕鈞. "Using Scoring Card Method for Predicting Polyketide Synthases and Nonribosomal Peptide Synthetases from Protein Sequences." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/68526411211692351625.
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碩士國立交通大學
生物資訊及系統生物研究所
102
Polyketide synthases (PKS) and nonribosomal peptide synthetases (NRPS) are accountable for the biosynthesis of two classes of pharmacologically active natural products. The biosynthesis of polyketides and nonribosomal peptides is an assembly-line like process. Those two large megaenzymes can be divided into several modules containing a set of functional domains and active sites. Each module is engaged in a complete cycle of polyketide or polypeptide chain elongation and relevant functional group modifications. By shuffling and modifying the modules, scientists could investigate the feasibility of producing artificial peptides by creating customizes polyketides and nonribosomal peptides. Besides, scientists need bioinformatics tool to reduce prime cost and time of discovery new functional proteins and its mechanism. For PKS/NRPS, most substrate specificity prediction tools that have been developed are based on recognition of the A and AT domains. However, some novel PKS and NRPS are being discovered that deviate from the canonical organization and can also include unusual domains. Those tools could not predict the sequences which do not composite of A and AT domains. Besides, only few researches aim at predicting query protein sequences to be PKS/NRPS or not. This work purpose a novel scoring card based method to solve this problem. Scoring card method uses dipeptide composition to estimate scores of sequences for predicting PKS/NRPS protein. SCM-PKS/NRPS calculates the propensities of 400 individual dipeptides using statistic discrimination between PKS/NRPS and non-PKS/NRPS proteins of a training data set. The propensity scores of all dipeptides are further optimized using an intelligent genetic algorithm. The score of a sequence is determined by the weighted sum of all propensity scores and dipeptides composition. SCM-PKS/NRPS for predicting PKS/NRPS achieves an accuracy of 89.52% for 10-fold cross-validation and a test accuracy of 82.84%. Additionally, more informative physicochemical properties of 20 amino acids are identified using the estimated propensity scores to characterize PKS/NRPS.
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Hsu, Kuen-Shan, and 許昆杉. "Establishment for Scoring Form of Cash Card Application-take a domestic bank as an example." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/28394112798295942384.
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Huang, Chi-Long, and 黃祈龍. "Establishment for Scoring Form of Credit Card Application-take a domestic bank as an example." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/61180967026396069824.
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高德芬. "Scoring Card of dipeptides for predicting solubility of recombinant proteins in E. coli expression system." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/12326627385350722786.
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碩士國立交通大學
生物資訊及系統生物研究所
99
Protein expression system is a very common and useful experiment skill in protein studying. Nowadays, Escherichia coli (E. coli) are mostly universal hosts for cloning and expressing in a broad of researches with its fast and inexpensive characters. However, there is a serious obstacle in protein expression system. Many proteins are produced in the form of insoluble aggregation that is a major obstruct for a lot of experiments, and the misfolded aggregation is called inclusion body. Accordingly, researchers usually do their best to get the soluble form of protein via regulating experimental conditions, but the processes are still trial-and-error. Many recent researches did their effort to predict the solubility of expressed proteins in E. coli via support vector machine (SVM). Existing methods applied a wide variety of primary structure feature sets, including physical chemical index and composition of amino acid, dipeptide and tripeptide. Generally, the prediction models and results using a black-box like method, such as SVM, are not easily interpretable. This study investigated several feature types and then proposed a scoring card method of dipeptides to predict the solubility of expressed proteins in E. coli. The proposed scoring card is a very intuitive prediction method that uses dipeptide statistic to construct a scoring matrix. Every input sample can get a score according to this scoring matrix, and then a best cut-off value was chosen from the validation data. Furthermore, to improve the scoring card method, an intelligent genetic algorithm (IGA) is used to optimize the scoring matrix, in which it can get a better performance of ROC curve to promote the classification accuracy. The IGA-scoring card could yield an accuracy of 81.7%, higher than 76.9% of using an SVM method using the same dataset. Finally, the better accuracy and more efficient classification result could be confirmed by the comparison among SVM, scoring card and IGA-scoring card for this problem of classification between expressed proteins.
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Lee, Li-Chuen, and 李麗娟. "An empirical study of using data mining techniques to credit scoring model for credit card." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/25624567352427864407.
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碩士國立高雄應用科技大學
商務經營研究所
95
Recently years, owing to inappropriate credit risk management, the credit card issuing banks in Taiwan have resulted in huge amount of losses, which induces the credit-risk assessment to become a critical work. Nowadays, the government of Taiwan has established legislation, which indicates the credit card issuing banks must class credit states of credit card holders to certain level of credit risk by according to periodically reexamining. However, it is a hard work to make elaborately reassessment for enormous credit card holders efficiently by limited cost. Therefore, it needs an efficient method to automatically distinguish classes of card holders, and the credit scoring is a feasible way to perform the task. In this study, we have adopted four methods to model credit scoring. This work has adopted LDA to select variables from dataset, and then used back-propagation neural networks to build a credit scoring model for achieving reassessment of credit card holders. Such approach we called it as a hybrid method. Moreover, the LDA, decision tree, and neural network models are re-implemented to compare with the hybrid one. Through the comparing results, it expects to find out the most appropriate model, which can deal with credit card data effectively. The ultimate goal of this work is to use the appropriate model in bank for achieving automatic credit card class reassessment. The experimental results are revealed below: 1.The hybrid model has higher performance than NN model based on accuracy and specificity. The experimental result reveals that the design of hybrid method can mitigate the property of GIGO of neural network. 2.The decision tree has the highest value of accuracy and sensitivity and the characteristic of easy to use and interpret. This study finally choose the decision tree as the most appropriate credit scoring model to achieve automatic credit risk reassessment for credit card holders. 3.In this article, a two-stage reassessment procedure is constructed to solve credit risk reassessment for credit card holders.
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Tsai, Tsai-Chieh, and 蔡采潔. "Developing a novel cancer scoring card method for predicting metastasis of colorectal cancer from lymph node biopsy." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/uszmme.
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碩士國立交通大學
生物資訊及系統生物研究所
107
Background: Colorectal cancer is the third most common cancer in the world. In Taiwan, it ranks as the top ten cancers. Traditionally, the staging of colorectal cancer requires pathologists to observe whether the lymph nodes around the large intestine are invaded by cancer cells (lymphatic metastasis). However, the section is usually cut in the middle of the lymph node, sometimes the tumors have not been cut during the making of the slide, so the pathologist cannot observe the presence of cancer cells in this lymph node. The more thorny issue is that patients with stage 3 misdiagnosed as stage 1 or 2. The existing prediction methods almost focus on the detection of cancer cells, and less on the quantification of the degree of metastasis of cancer cells in histological images, or the image analysis method for effective examination of initial lymph node metastasis. Methods: This study proposes a novel cancer scoring card (CSC) method, which uses the weights of each image feature to build a scorecard, and further optimized using an intelligent genetic algorithm. To apply image analysis of H&E stained colorectal lymph node slides. The degree of metastasis is determined by the sum of the feature weights. The system can provide interpretable results such as image scores and features to assist the doctor in interpreting the lymph nodes for metastasis. The source of the slides was provided by the China Medical University Hospital. The number of patches trained was 2443 (8 lymph nodes). Results: The number of test patches without and with cancer cell invasion was 1208 vs. 399 (7 lymph nodes), the area under the curve (AUC) of support vector machine (SVM) is 0.9995, and the AUC of CSC is 0.9992. Conclusion: The results show that the CSC is comparable to the support vector machine, and the CSC is not only simple and intuitive to classify, but also mutually validated by giving the feature weights and biological meanings, so the classifier and features are interpretable. The visualized heatmap obtained from the CSC can quantify the microenvironment of lymph nodes, thereby reminds the doctors that there may be metastases in other regions of the lymph node for the purpose of assisting diagnosis and increase the probability of detecting early lymph node metastasis in colorectal cancer.
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陳國棟. "A Study of Maintenance Performance Indices for Taipei Metro Electrical Multiple Unit Using Balanced Scoring Card Method." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/97012882961223803959.
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碩士元智大學
工業工程與管理學系
92
ABSTRACT This thesis develops maintenance performance indices (MPIs) using balanced scoring card method for Taipei Metro Electrical Multiple Units (EMU). After data collection and analysis from its managers and employees, 20 critical MPI’s are decided and five core performance indices are obtained. They are financial items as cost, equipment benefit, and maintenance personnel efficiency, customer’s satisfactory items as safety and amenity, internal procedural items as reliability and maintenance works accomplishing percentage, and developing items as employee learning and expertise developing. Single factor variation analysis shows no significant cognition difference between managers and employees no matter what are their duties, years of experience, or education. Although many Repair Request/Work Order have been collected in Taipei Metro EMU, it is insufficient to evaluating developed MPI’s. Therefore, associated procedures and statistical/analytical sheets are suggested in this thesis. This can make the MPI’s easily integrate to the current system of Taipei Metro EMU.
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Cheng, Yu Hsien, and 鄭鈺銑. "A study for scoring model of personal Credit Card application－take a domestic bank as an example." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/91410387878193555513.
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碩士國立臺北大學
企業管理學系碩士在職專班
98
In order to reduce the burden of card revolvers, Financial Supervisory Commission regulates the banks to provide either personal credit loans with amortized payment schedule or credit loans to credit balance bearers with more than one year on-time payment history. Accordingly, banks need to consider both the revenue and risk management based on the applicant’s credit worthiness before approving the credit line. Thus, card issuing banks should build up the scorecard system to reduce the delinquent rate. The objective of this research is to study the explanatory power of the variables in the credit model, and build up the credit card’s predicting model. To testify how much ratio the rejecting applicants to explain the model, therefore, the bank A could make profit. A sample of 49,331 credit card applicants, including approved and rejected, were collected from Bank A. Sampling period started from May 2008 to August 2009. A set of 24 explanatory variables were developed based on external data from JCIC and internal credit data of Bank A. Using the logistic regression, we explore the important factors of credit card characteristics. The results summarize as follows: 1. There are 19 independent variables that have the ability to explain the model, and to build up the predicting model of credit card. 2. According to the model, there are 94.92% rejected applicants who could become the approved. Thus, the bank could make profit if the rejecting rate decreases. 3. The sum of JCIC outer score and inner score has the ability to explain the model.
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Chang, Chen-Chih, and 張振志. "Data Mining in the Application of Behavior Scoring– Empirical Results from a Credit Card Issuing Bank in Taipei." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/48294522369413895450.
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碩士輔仁大學
管理學研究所
95
The development of application scoring in domestic credit card market has been trending toward maturity and delicacy, but, nevertheless, the credit risk management of current cardholders has been accusing of shortage and neglect. Accordingly, this study aims to construct the behavioral scoring model for practitioners in banking industry using the superior and most accurately techniques among the traditional discriminant analysis, logistic regression, neural networks and two-stage hybrid method for customer classification, each of which employs 46 independent variables associated with the aspects of demography, delinquency, payment, credit utilization and cash utilization to comprehensively profile customers and attempt to capture the patterns of cardholders’ repayment behaviors in history, periodically recognize the their credibility and ultimately predict the current customers’ future reimbursement status ensuring the improvement of credit management measures for financial institutes. Results of this empirical study obtained both by discriminant analysis and logistic regression manifest the significance of 11 selected variables in the proposed behavioral scoring models, such as the average utilization last 3 month, number of times M1+ last 6 month, loan flag, number of loan, maximal credit card time of association, age, percentage of cash advance to credit line, balance transfer card flag, days since last payment, education, average utilization last 6 month. The current paper concludes that the hybrid method outperforms all the rest 3 techniques since its classification accuracy reaches 84.00%, higher than neural network of 83.20%, logistics regression of 80.60% and discriminant analysis of 80.07%, and is suggested to be utilized for the future related analysis.
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Su, Yi-Jing, and 蘇怡靜. "Integrating ICA and DEA Models to Build and Analysis of customers from the Credit Card Behavior Scoring Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/5pn7e5.
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碩士國立臺北科技大學
商業自動化與管理研究所
96
The competition of .finance industry is more and more vigorous. For most of the banking organizations, credit card business is considered highly important among their consumers businesses. The effectiveness of the credit cards issued directly impact the profits of the banks. Therefore, it becomes one of the most important topics to operate a customer relationship management to consolidate those valuable customers. Industry should be to improve purchasing amount of each customer as the goal and raise interest rates of the Bank. This study will estimate the label of customers from the credit card behavior of usage by using DEA. Then we analyze the weights of DEA by using ICA. It interprets the characteristic of each DMU. Moreover we can use the results to adjust credit lines revolving expenses and .procedures expenses.
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Hsiao, Xiang-ru, and 蕭翔如. "A study of donation behavior credit card cardholders in Kaohsiung city－to transfer the donation take the Scoring Points as an example." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/qe6w97.
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