• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.9-14
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• 2021

This paper predicted a model that indicates whether to buy a car based on primary health insurance customer data. Currently, automobiles are being used to land transportation and living, and the scope of use and equipment is expanding. This rapid increase in automobiles has caused automobile insurance to emerge as an essential business target for insurance companies. Therefore, if the car insurance sales are predicted and sold using the information of existing health insurance customers, it can generate continuous profits in the insurance company's operating performance. Therefore, this paper aims to analyze existing customer characteristics and implement a predictive model to activate advertisements for customers interested in such auto insurance. The goal of this study is to maximize the profits of insurance companies by devising communication strategies that can optimize business models and profits for customers. This study was conducted through the Microsoft Azure program, and an automobile insurance purchase prediction model was implemented using Health Insurance Cross-sell Prediction data. The program algorithm uses Two-Class Logistic Regression and Two-Class Boosted Decision Tree at the same time to compare two models and predict and compare the results. According to the results of this study, when the Threshold is 0.3, the AUC is 0.837, and the accuracy is 0.833, which has high accuracy. Therefore, the result was that customers with health insurance could induce a positive reaction to auto insurance purchases.

• Environmental and Resource Economics Review
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• v.21 no.4
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• pp.921-945
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• 2012

Conditional Logit (CL) model is widely used since its model estimation and interpretation of results of the model is relatively easy, on the other hand, it has the limit of preference heterogeneity of respondents being not fully considered. In this study we used the two models, Mixed Logit (ML) Model and Latent Class Model (LCM) to explain preference heterogeneity of respondents for protection for Boryeong Dam wetland. As a result of the examination for heterogeneity in Boryeong city and six metropolitan areas, we found there was significant difference between two regions. While there was explicit preference heterogeneity within respondents in Boryeong city, we found little heterogeneity within respondents in six metropolitan areas. Thus in the case of six metropolitan areas, CL model can be used for parameter estimation while in the case of Boryeong city, WTP estimates are based on parameter estimates from ML model to reflect the heterogeneity within respondents. Additionally, ML model with interaction and 2-class LCM for respondents in Boryeong city were used to explain the sources of the heterogeneity. The ML model with interaction has advantage of explaining individual unobserved heterogeneity. However The comarison between these two models reflects the fact that LCM provided added information that was not conveyed in the ML model with interaction. Thus, Preference heterogeneity within respondents in this study may be better explained by class level through LCM rather than indiviual level through ML model.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.271-280
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• 2009

For bivariate binomial data with both intra and inter-class correlation, Danaher and Hardie (2005) proposed a bivariate beta-binomial model. However, the model is limited to the situation where the intra-class correlation is strictly positive. Thus it might be seriously inadequate for data with a negative intra-class correlation. Several authors have considered generalized binomial distributions covering a wider range of intra-class correlation which could relax the possible model restrictions imposed. Among others there are the additive/multiplicative and the beta/extended beta binomial model. In this study, bivariate models of the Sarmanov (1966) type are formed by combining each of those univariate models to take care of the inter-class correlation, and are evaluated in terms of the goodness-of-fit. As a result, B-mB and B-ebB are fitted, successfully, to real data and that B-mB, which has a wider permissible range than B-ebB for the intra-class correlation is relatively preferred.

• Journal of Gifted/Talented Education
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• v.24 no.4
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• pp.657-677
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• 2014

Considering the expansion of gifted education and the quantitative increase the Gifted Science Academy, it is important to seek the appropriate methods of mathematics teaching for gifted high school students. In particular, to reflect current trends in mathematics education that the mathematical creativity is being presented as an important educational goal, Now is the time we need student-centered discussion model for regular mathematics classes, not teacher-centered instruction in the way of knowledge transfer. In this study, class model of preparation-based discussion was designed and applied to the regular mathematics classes for the Science Academy. Students participating in this research had a lot of pressure in preparation activities for discussion, but they said that the discussion compared to traditional lecture was mathematically meaningful experience. These findings suggest the implication that class model of preparation-based discussion can be meaningfully applied to the regular mathematics class.

• Journal of KIISE
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• v.42 no.6
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• pp.817-822
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• 2015

Relation extraction is an important information extraction technique that can be widely used in areas such as question-answering and knowledge population. Previous studies on relation extraction have been based on supervised machine learning models that need a large amount of training data manually annotated with relation categories. Recently, to reduce the manual annotation efforts for constructing training data, distant supervision methods have been proposed. However, these methods suffer from a drawback: it is difficult to use these methods for collecting negative training data that are necessary for resolving classification problems. To overcome this drawback, we propose a one-class classification model that can be trained without using negative data. The proposed model determines whether an input data item is included in an inner category by using a similarity measure based on lexical information and syntactic patterns in a vector space. In the experiments conducted in this study, the proposed model showed higher performance (an F1-score of 0.6509 and an accuracy of 0.6833) than a representative one-class classification model, one-class SVM(Support Vector Machine).

• Journal of Wetlands Research
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• v.15 no.3
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• pp.357-366
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• 2013

This study investigates to explain preference heterogeneity of respondents for economic valuation in washland of Upo wetland using Mixed Logit Model and Latent Class Model. Mixed Logit Model showed respondent heterogeneity in the attributes of wetland area and funds as well as some alternatives violated IIA assumption. 2-class Latent Class Model for respondents were used to explain the sources of the heterogeneity. Class 1 respondents who are located relatively close to Upo wetland had more experience and knowledge of Upo wetland and better understood the information suggested in the questionnaire than class 2 respondents in mostly metropolitan area of Seoul, Incheon.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2350-2364
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• 1999

In this paper, we propose a method of class library repository design for provide reuser the object-oriented C++ class component. To class library design, we started by studying the characteristics of a reusable component. We formally defined the reusable component model using an entity relationship model. This formal definition has been directly used as the database schema for storing the reusable component in a repository. The reusable class library may be considered a knowledge base for software reuse. Thus, we used that Enumerative classification of breakdown of knowledge based. And another used classification is clustering of based on class similarity. The class similarity composes member function similarity and member data similarity. Finally, we have designed class library for hierarchical inheritance mechanism of object-oriented concept Generalization, Specialization and Aggregation.

• Journal of Ship and Ocean Technology
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• v.9 no.2
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• pp.29-36
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• 2005

A hull form of Baltic ice class IA Aframax tanker has been developed taking into consideration of powering performance in brash ice channels based on IA class rules. Speed performance of the ship hull form in normal seagoing has been validated through model tests in a towing tank. The hull form design developed in this work has demonstrated good speed performance in normal seagoing although the ship design is entitled to ice class IA.

• Journal of the Korean Society of Radiology
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• v.12 no.2
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• pp.225-231
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• 2018

Recently, new teaching methods for communicating with teachers and students have been emerged according to the trends of decreasing the school-age population and the development of the mass media. We have applied teaching-learning model based on the flip learning to the college students in this work. As a result of the test for the customized flipped learning teaching-learning model in pre-class, the attendance rate of the major subject was 92.3% whereas that in liberal arts courses other than majors revealed 87.6%. This result for attendance rate shows that first year students in the radiology department have been actively participated in pre-class of the major subject than that of the liberal arts curriculum. From comparing the differences between the study group that was applied flipped learning in class and the non-applied group, the research group showed higher scores in knowledge, skills, and attitudes than the comparative group. In addition, more than 90% of the learners improved their responsibility, problem solving ability, creative thinking, cooperative ability, and communication ability through this learning program. From the test for the difference in the role of radiologists in the post class, the mean score was 4.40 for the group applied the teaching-learning model while that for non-applied group was 2.10. Hence, from such results, we see that this teaching-learning model is appropriate and needs to be extended to cultivate basic skills in radiology and relevant vocational education.

• Journal of the Korea Computer Industry Society
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• v.4 no.4
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• pp.607-612
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• 2003

This paper has been designed extend object-orientid database model that introducted new class basing the Heurilistic Classfication model. In order to implement this model, we have introducted heurilistic class to traditional object-orinted database. And we designed querry for search data that basis on the heurilistic classficasion model using stored data in extened object-oriend data model.