• Journal of Industrial Convergence
• /
• v.16 no.4
• /
• pp.33-46
• /
• 2018

Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks.

• The Korean Journal of Applied Statistics
• /
• v.18 no.3
• /
• pp.543-553
• /
• 2005

Linear logistic regression is one of the most widely used method for credit scoring in credit risk management. This paper deals with credit scoring using splines based on Logistic regression. Linear splines and an automatic basis selection algorithm are adopted. The final model is an example of the generalized additive model. A simulation using a real data set is used to illustrate the performance of the spline method.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.3
• /
• pp.1643-1647
• /
• 2013

Background: To identify the risk factors and assess the role of survivin in predicting progessivity precancerous cervical lesions. Materials and Methods: This case-control study was conducted from October 2009 until May 2010. We obtained 74 samples, classified according to the degree of cervical intraepithelial neoplasia (CIN): 19 samples for CIN 1, 18 samples for CIN 2, 18 samples for CIN 3, and 19 samples as controls. Demographic profiles and risk factors assesment, histopathologic examination, HPV DNA tests, immunocytochemistry (ICC) and immunohistochemistry (IHC) staining for survivin expression were performed on all samples. Data was analyzed with bivariate and multivariate analysis. Results: Multivariate analysis revealed significant risk factors for developing precancerous cervical lesions are age <41 years, women with ${\geq}2$ sexual partners, course of education ${\geq}13$ years, use of oral contraceptives, positive high-risk HPV DNA, and high survivin expression by ICC or IHC staining. These factors were fit to a prediction model and we obtained a scoring system to predict the progressivity of CIN lesions. Conclusions: Determination of survivin expression by immunocytochemistry staining, along with other significant risk factors, can be used in a scoring system to predict the progressivity of CIN lesions. Application of this scoring system may be beneficial in determining the action of therapy towards the patient.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.6
• /
• pp.1285-1303
• /
• 2019

In the current credit scoring system, the credit bureau gathers credit information from financial institutions and calculates a credit score based on it. However, because all sensitive credit information is stored in one central authority, there are possibilities of privacy violations and successful external attacks can breach large amounts of personal information. To handle this problem, we propose privacy-preserving credit scoring in which a user gathers credit information from financial institutions, calculates a credit score and proves that the score is calculated correctly using a zero-knowledge proof and a blockchain. In addition, we propose a zero-knowledge proof scheme that can efficiently prove committed inputs to check whether the inputs of a zero-knowledge proof are actually provided by financial institutions with a blockchain. This scheme provides perfect zero-knowledge unlike Agrawal et al.'s scheme, short CRSs and proofs, and fast proof and verification. We confirmed that the proposed credit scoring can be used in the real world by implementing it and experimenting with a credit score algorithm which is similar to that of the real world.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 1999.05a
• /
• pp.205-209
• /
• 1999

일반화된 지수생존모형(generalized exponential survival model)을 고려하여 이 모형의 모수를 추정하는 수정된 FS(modified Fisher scoring)방법을 제안한다. 이를 위해 우도방정식(likelihood equation)을 유도하고 초기추정치 (initial estimate)를 포함한 추정알고리즘(estimating algorithm)을 개발한다.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.05
• /
• pp.430-433
• /
• 1997

We present the preliminary algorithms for automatic sleep scoring. According to the Rechtschaffen & Kales[3]'s critera, we developed six events detectors and eight parameters which contain the background information of signals, such as EEG, EMG, EOG. With the calculated parameters, we scored each epoch by IF-THEN rules, ANFIS for REM preiods, and finally Neural Network for unobvious epochs. The typical point of this algorithm is that the epoch which had good data sets were calculated in the first stage, and unobvious epochs were postponed until the final stage. After staging the good epochs, we classified unobvious epochs by the dominant stage of previous and posterior epochs.

• Journal of Integrative Natural Science
• /
• v.6 no.2
• /
• pp.100-103
• /
• 2013

Using computational approaches we can dock small molecules into the structures of Macromolecular targets and then score their potential complementarity to binding sites is widely used in hit identification and lead optimization techniques. This review seeks to provide the application of docking in structure-based drug design (binding mode prediction, Lead Identification and Lead optimization), and also discussed how to manage errors in docking methodology in order to overcome certain limitations of docking and scoring algorithm.

• Nuclear Engineering and Technology
• /
• v.42 no.4
• /
• pp.414-425
• /
• 2010

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into "families". On the basis of the decision maker's preferences, each family is then synthetically represented by a "head of the family" solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.25 no.6
• /
• pp.690-699
• /
• 2014

Jet engine modulation(JEM) is a technique used to identify the jet engine type from the radar target signature modulated by periodic rotation of the jet engine mounted on the aircraft target. As a new approach of JEM, this paper proposes an automatic algorithm for extracting the jet engine information. First, the rotation period of the jet engine is yielded from auto-correlation of the JEM signal preprocessed by complex empirical mode decomposition(CEMD). Then, the final blade number is estimated by introducing the DM(Divisor-Multiplier) rule and the 'Scoring' concept into JEM spectral analysis. Application results of the simulated and measured JEM signals demonstrated that the proposed algorithm is effective in accurate and automatic extraction of the jet engine information.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.280-282
• /
• 2005

A ribonucleic acid (RNA) is one of the two types of nucleic acids found in living organisms. An RNA molecule represents a long chain of monomers called nucleotides. The sequence of nucleotides of an RNA molecule constitutes its primary structure, and the pattern of pairing between nucleotides determines the secondary structure of an RNA. Non-coding RNA genes produce transcripts that exert their function without ever producing proteins. Predicting the secondary structure of non-coding RNAs is very important for understanding their functions. We focus on Nussinov's algorithm as useful techniques for predicting RNA secondary structures. We introduce a new traceback matrix and scoring table to improve above algorithm. And the improved algorithm provides better levels of performance than the originals.