• Journal of The Korean Association For Science Education
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• v.41 no.3
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• pp.203-220
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• 2021

We explored the performance improvement method of automated scoring for scientific argumentation. We analyzed the pattern of argumentation using automated scoring models. For this purpose, we assessed the level of argumentation for student's scientific discourses in classrooms. The dataset consists of four units of argumentation features and argumentation levels for episodes. We utilized argumentation clusters and n-gram to enhance automated scoring accuracy. We used the three supervised learning algorithms resulting in 33 automatic scoring models. As a result of automated scoring, we got a good scoring accuracy of 77.59% on average and up to 85.37%. In this process, we found that argumentation cluster patterns could enhance automated scoring performance accuracy. Then, we analyzed argumentation patterns using the model of decision tree and random forest. Our results were consistent with the previous research in which justification in coordination with claim and evidence determines scientific argumentation quality. Our research method suggests a novel approach for analyzing the quality of scientific argumentation in classrooms.

• Environmental Analysis Health and Toxicology
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• v.24 no.2
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• pp.95-105
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• 2009

Chemical Ranking and Scoring (CRS) system is a useful tool to screen priority chemicals of large body of substances. The relative ranking of chemicals based on CRS system has served as a decision-making support tools. Exposure potential and toxicity are significant parameters in CRS system, and there are differences in evaluating those parameters in each CRS system. In this study, the parameters of exposure potential, human toxicity, and ecotoxicity were extensively compared. In addition the scoring methods in each parameter were analyzed. The CRS systems considered in this study include the CHEMS-1 (Chemical Hazard Evaluation for Management Strategies), SCRAM (Scoring and Ranking Assessment Model), EURAM (European Union Risk Ranking Method), ARET (Accelerated Reduction/Elimination of Toxics), and CRS-Korea. An comparative analysis of the several CRS systems is presented based on their assessment parameters and scoring methods.

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

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.282-291
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• 2022

This research applies a pre-trained bidirectional encoder representations from transformers (BERT) handwriting recognition model to predict foreign Korean-language learners' writing scores. A corpus of 586 answers to midterm and final exams written by foreign learners at the Intermediate 1 level was acquired and used for pre-training, resulting in consistent performance, even with small datasets. The test data were pre-processed and fine-tuned, and the results were calculated in the form of a score prediction. The difference between the prediction and actual score was then calculated. An accuracy of 95.8% was demonstrated, indicating that the prediction results were strong overall; hence, the tool is suitable for the automatic scoring of Korean written test answers, including grammatical errors, written by foreigners. These results are particularly meaningful in that the data included written language text produced by foreign learners, not native speakers.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.395-397
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• 1997

We have applied the neural network method for the neural networkmethod for the automatic scoring of the sleep stage. 17 features are extracted from the recorded EEG, EOG and EMG signals. These features are inputed to tile multilayer perceptron model. Neural network was trained with error-back propagation method. Results are compared with manual scoring of the experts, and show the possibility of application of automatic method in sleep stage scoring.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.279-286
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• 2005

The objective of this study is to suggest a credit scoring model of a capital company's customers by integration of multiple classifiers using genetic algorithm. For this purpose , an integrated model is derived in two phases. In first phase, three types of classifiers MLP (Multi-Layered Perceptron), RBF (Radial Basis Function) and linear models - are trained, in which each type has three ones respectively so htat we have nine classifiers totally. In second phase, genetic algorithm is applied twice for integration of classifiers. That is, after htree models are derived from each group, a final one is from these three, In result, our suggested model shows a superior accuracy to any single ones.

• Journal of Korean Society of Rural Planning
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• v.21 no.2
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• pp.115-126
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• 2015

Although the frequency of use for community facilities in rural villages is growing as well as the importance of the facilities for urban-rural exchange is being highlighted, study on spatial location-analysis of the facilities for such multi-purpose is not so much. This study aims to find the spatial distribution forms of community facilities in rural villages such as community center and rural-pocket park through location-analysis, in order to provide available data for selecting location in the future. As the study area, Sojeong-myeon, Sejong Special City was selected. This study conducted GIS analysis for criteria of the location-evaluation model developed in this study. This study introduced the concept of time-distance for accessibility analysis. This paper also used linear-consecutive scoring method(LCSM) as a scoring method of criteria and Analytic Hierarchy Process(AHP) method for weighting values of criteria. The application results showed that the new model can generate the intensity of community facilities according to spatial distribution and accessibility from cities to the facilities.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.624-626
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• 2023

Credit scoring is a technique used by financial institutions to assess the creditworthiness of potential borrowers. This involves evaluating a borrower's credit history to predict the likelihood of defaulting on a loan. This paper presents an ensemble of two Transformer based models within a framework for discriminating the default risk of loan applications in the field of credit scoring. The first model is FinBERT, a pretrained NLP model to analyze sentiment of financial text. The second model is FT-Transformer, a simple adaptation of the Transformer architecture for the tabular domain. Both models are trained on the same underlying data set, with the only difference being the representation of the data. This multi-modal approach allows us to leverage the unique capabilities of each model and potentially uncover insights that may not be apparent when using a single model alone. We compare our model with two famous ensemble-based models, Random Forest and Extreme Gradient Boosting.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.239-256
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• 2020

Health is an important issue due to increased life expectancy. As a result, the demand for industry and services associated with individual health, health-related programs and services will be facilitated by a method to evaluate and classify the health level of an individual based on various factors. This study suggests a methodology to measure and score an individual health level. A credit scoring model was introduced to implement the categorization of variables, construct a prediction model, and to score individual health level. Cohort DB provided by National Health Insurance Service was used to illustrate overall procedures. It is expected that the suggested model can be utilized in designing and managing health care services as well as other health-related programs.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.1-16
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• 2009

Recently, most companies are speeding up the movement of modeling and strategically applying IDI (Integration Database Information). This is due to the fact that CRM (Customer Relationship Management) representing communication and relation maintenance with customers, has been raised one of the most important issues for companies. From this point of view, this study is to develop the scoring campaign system connect-ing customer scoring model to marketing layer through data mining techniques which are the core factor for CRM. This developed system makes users easily choose the target customers as well as easily obtain customer scoring results under GUI circumstances which helps users easily apply as a result.