• The Journal of Information Systems
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• v.18 no.3
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• pp.375-399
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• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

• Journal of ILASS-Korea
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• v.11 no.2
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• pp.75-80
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• 2006

The present article proposes a new droplet collision model including the stretching separation regime and the formation of satellite droplets. The new model consists of several equations to calculate the post-collision characteristics of colliding droplets and satellite droplets. These equations are derived from the energy balance of droplets between before and after collision. For binary collision of water droplets, the new model shows good agreement with experimental data far the number of satellite droplets.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.375-380
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• 2003

This study is concerned with the machine part grouping m cellular manufacturing. To group machines into the set of machine cells and parts into the set of part families, new p-median model considering the production data such as the operation sequences and production volumes for parts is proposed. Unlike existing p-median models relying on the classical binary part-machine incidence matrix which does not reflect the real production factors which seriously impact on machine-part grouping, the proposed p-median model reflects the production factors by adopting the new similarity coefficient based on the production data-based part-machine incidence matrix of which each non-binary entry indicates actual intra-cell or inter-cell flows to or from machines by parts. Computation test compares the proposed p median model favorably.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.75-89
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• 1996

This paper proposes a goodness-of-fit test for checking the adequacy of the additive risk model with a binary covariate. The test statistic is based on martingale residuals, which is the extended form of Wei(1984)'s test. The proposed test is shown to be consistent and asymptotically normally distributed under the regularity conditions. Furthermore, the test procedure is illustrated with two set of real data and the results are discussed.

• Journal of the Korean Society for Railway
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• v.14 no.4
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• pp.383-389
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• 2011

Until now, in planning and constructing KTX and the Express Way, the connectivity and transfer between these facilities have not been considered. In this study the effect of mode choice behavior by connecting KTX and the Express Way is analyzed through estimating Multinomial Logit Model and Binary Logit Model. The SP and RP surveys to develop these models were carried out and the data were selected from the passengers using the KTX station, Express Bus Terminals and Rest Areas in the Express Way. To test the effect of connectivity and transfer in the field, the case study for Dongtan KTX station was carried out. According to the results, connecting the KTX station and the Express Way has the effect of increasing the demand by 30%. And this is caused by saving about 120 minutes of traveling time from Seoul to Pusan. This study shows that the connectivity and transfer can increase the efficiency of transportation system and the improvement in the mobility and accessibility will maximize the usages of these two facilities.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.467-484
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• 2024

Longitudinal data are measured repeatedly over time from the same subject, so there is a correlation from the repeated outcomes. Therefore, when analyzing this correlation, both serial correlation and between-subject variation must be considered in longitudinal data analysis. In this paper, we will focus on the marginalized models to estimate the population average effect of covariates among models for analyzing longitudinal binary data. Marginalized models for longitudinal binary data include marginalized random effects models, marginalized transition models, and marginalized transition random effect models, and in this paper, these models are first reviewed, and simulations are conducted using complete data and missing data to compare the performance of the models. When there were missing values in the data, there is a difference in performance depending on the model in which the data was generated. We analyze Korea Health Panel data using marginalized models. The Korean Medical Panel data considers subjective unhealthy responses as response variables as binary variables, compares models with several explanatory variables, and presents the most suitable model.

• Journal of the Korea Computer Industry Society
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• v.2 no.2
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• pp.203-210
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• 2001

To estimate relational models and test the theoretical hypotheses of binary tree search algorithms, we built binary search trees with random permutations of n (number of nodes) distinct numbers, which ranged from three to seven. Probabilities for building binary search trees corresponding to each possible height and balance factor were estimated. Regression models with variables of number of nodes, height, and average number of comparisons were estimated and the theorem of O(1g(n)) was accepted experimentally by a Lack of Test procedure. Analysis of Variance model was applied to compare the average number of comparisons with three groups by height and balance factor of the trees to test theoretical hypotheses of a binary search tree performance statistically.

• Journal of Astronomy and Space Sciences
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• v.20 no.4
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• pp.249-260
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• 2003

CCD photometric observations of a W UMa type contact binary AH Cnc were performed for ten nights from December 1998 to May 1999 using a PM512 CCD camera and BVI filters attached to the 61㎝ reflector at Sobaeksan Optical Astronomy Observatory. New BVI light curves were analyzed with contact Mode 3 of the Wilson-Devinney binary model. We obtained photometric solutions and Roche geometry of this binary system. Through the analysis of the (O-C) diagram with all times of minimum light published so far and including hour's secular variations of orbital period and the mass transfer rate were calculated.

• Korean Chemical Engineering Research
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• v.43 no.3
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• pp.371-379
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• 2005

Adsorption equilibria of the gases $H_2$, $CH_4$, and $C_2H_4$ and their binary mixtures on activated carbon (Calgon co.) have been measured by static volumetric method in the pressure range of 0 to 18 atm at temperatures of 293.15, 303.15, and 313.15 K. From the parameters obtained from single component adsorption isotherm, multi-component adsorption equilibria could be predicted and compared with experimental data. The binary experimental data were applied to four models : extended Langmuir, extended Langmuir-Freundlich, Ideal Adsorbed Solution theory (IAST), and Vacancy Solution Model (VSM). The models were found to describe the experimental data with a reasonable accuracy. Extended L-F model predicts equilibria of mixture better than any other model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.56-62
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• 2021

High forecast accuracy is required as social issues on particulate matter increase. Therefore, many attempts are being made using machine learning to increase the accuracy of particulate matter prediction. However, due to problems with the distribution of imbalance in the concentration and various characteristics of particulate matter, the learning of prediction models is not well done. In this paper, to solve these problems, a binary classification model was proposed to predict the concentration of particulate matter needed for prediction by dividing it into two classes based on the value of 80㎍/㎥. Four classification algorithms were utilized for the binary classification of PM10. Classification algorithms used logistic regression, decision tree, SVM, and MLP. As a result of performance evaluation through confusion matrix, the MLP model showed the highest binary classification performance with 89.98% accuracy among the four models.