• The KIPS Transactions:PartA
• /
• v.10A no.2
• /
• pp.131-136
• /
• 2003

Comparison criteria and a selection model for concurrency control algorithms has been presented in this article. In addition, a comparison analysis has been performed with the developed comparison model. The result of the analysis can be utilized to select the best fitting concurrency control algorithm to the user's existing system environment.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.10a
• /
• pp.598-600
• /
• 2001

DNA chip technology enables us to monitor thousands of gene expressions per sample simultaneously. Typically, DNA microarray data has at least several thousands of variables (genes) wish relatively smal1 number of samples. Thus feature (gene) selection by dimensionality reduction is necessary for efficient data analysis. In this paper we employ the partial least squares (PLS) method for gene selection and the principal component analysis (PCA) method for classification. The useful behavior of the PLS is verified by computer simulations.

• Communications for Statistical Applications and Methods
• /
• v.26 no.2
• /
• pp.79-89
• /
• 2019

Classification models pertaining to receiver operating characteristic (ROC) curve analysis have been extended from univariate to multivariate setup by linearly combining available multiple markers. One such classification model is the multivariate ROC curve analysis. However, not all markers contribute in a real scenario and may mask the contribution of other markers in classifying the individuals/objects. This paper addresses this issue by developing an algorithm that helps in identifying the important markers that are significant and true contributors. The proposed variable selection framework is supported by real datasets and a simulation study, it is shown to provide insight about the individual marker's significance in providing a classifier rule/linear combination with good extent of classification.

• Culinary science and hospitality research
• /
• v.15 no.3
• /
• pp.179-196
• /
• 2009

As consumers are more conscious about the preferred traits and branding in restaurant selection, restaurant practitioners are getting more interested in the traits which constitute and influence consumers' brand association. Dining selection is known to be a complicated psychological process that consumers undergo, incorporating social and cultural values, personal tastes, and other psychological factors. This study was designed to investigate the specific non-food traits perceived by consumers in fine restaurant selection. Also, it prioritized those examined traits. Building on exploratory qualitative research, results from 11 interviews through focus group sessions were utilized in data analysis. Content analysis of the interviews enabled categorization of the associations. The categorized variables were utilized for empirical data analysis of conjoint analysis. First, the results suggested five superior traits such as atmosphere, price, customer relations, employee competence, and convenience of systems in fine restaurant brand association. Second, they were prioritized in the order of atmosphere, price, consumer relations, employee competence, and convenience. Finally this study provided practical implications for fine restaurant professionals in linking consumers' psychologically networked traits to brand selection.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.3
• /
• pp.107-114
• /
• 2022

In this paper, we have enhanced the risk prediction of hypertension using the feature selection method in the Korean National Health and Nutrition Examination Survey (KNHANES) database of the Korea Centers for Disease Control and Prevention. The study identified various risk factors correlated with chronic hypertension. The paper is divided into three parts. Initially, the data preprocessing step of removes missing values, and performed z-transformation. The following is the feature selection (FS) step that used a factor analysis (FA) based on the feature selection method in the dataset, and feature importance (FI) and multicollinearity analysis (MC) were compared based on FS. Finally, in the predictive analysis stage, it was applied to detect and predict the risk of hypertension. In this study, we compare the accuracy, f-score, area under the ROC curve (AUC), and mean standard error (MSE) for each model of classification. As a result of the test, the proposed MC-FA-RF model achieved the highest accuracy of 80.12%, MSE of 0.106, f-score of 83.49%, and AUC of 85.96%, respectively. These results demonstrate that the proposed MC-FA-RF method for hypertension risk predictions is outperformed other methods.

• Korean Security Journal
• /
• no.27
• /
• pp.107-128
• /
• 2011

The purpose of this study was to determine the relationship between gymnasium selection and training adherence of security martial arts trainees. To this aim, this study selected 8 gymnasiums in Gyounggi and Incheon province and sampled 220 people above the fourth grade in elementary school. Out of these, 14 cases were dropped due to insufficient answers or incomplete answers, and 206 qualified cases were finally adopted for this study. Data were analyzed using exploratory factor analysis, reliability analysis and frequency analysis, MANOVA, correlation analysis and multiple regression analysis with SPSS 18.0. The results of this study were as follows. First, there were significant differences in gymnasium selection factors(instructor background, training program, public relations) depending upon the trainee's sex. Second, there were significant differences in gymnasium selection factors(training program, main others, public relations) depending upon the trainee's school grade. Third, there were no significant differences in training adherence factors depending upon the trainee's sex and school grade. Fourth, there were positive correlations between instructor background, training program and public relations-factors among gymnasium selection and management program, external and social relations-factors among training adherence. Fifth, the partial sub-factors of gymnasium selection have influenced training adherence. Consequently, security martial arts managers and instructors need to establish marketing strategies suitable for sex and school grade to recruit new security martial arts trainees. It may be considered that security martial arts instructors should construct the differentiated management system for trainees and the specialized training program for lasting training of new or existing security martial arts trainees.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.3
• /
• pp.83-92
• /
• 2004

Gene expression profile is numerical data of gene expression level from organism measured on the microarray. Generally, each specific tissue indicates different expression levels in related genes, so that we can classify cancer with gene expression profile. Because not all the genes are related to classification, it is needed to select related genes that is called feature selection. This paper proposes a new gene selection method using forward selection method in regression analysis. This method reduces redundant information in the selected genes to have more efficient classification. We used k-nearest neighbor as a classifier and tested with colon cancer dataset. The results are compared with Pearson's coefficient and Spearman's coefficient methods and the proposed method showed better performance. It showed 90.3% accuracy in classification. The method also successfully applied to lymphoma cancer dataset.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.19 no.2
• /
• pp.79-90
• /
• 2017

The main purpose of the study was to explore effects of selection criteria of collaborating fashion companies on the partnership growth of collaborating artists with those companies. Secondly was to find differences in the selection criteria of collaborating fashion companies and the partnership growth of collaborating artists according to the characteristics of artists including gender, age, frequency and time period of collaboration. The questionnaire was developed by the researchers and was collected from 50 artists with experience in collaboration with fashion companies. The questionnaire was composed of three parts including the selection criteria of fashion companies, partnership growth measured by a Likert-type scale, and characteristics of artists measured by a nominal scale. Data were analyzed by a frequency test, factor analysis, reliability test, regression analysis, and independent sample t-test using SPSS Win 18.0. The results of the study showed that significant effects of the selection criteria of collaborating fashion companies on the partnership growth of collaborating artists with the companies. Also, there were differences in the selection criteria of collaborating fashion companies and the partnership growth of collaborating artists between artist groups according to the characteristics of the artists including gender, age, frequency and time period of collaboration.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.5
• /
• pp.1099-1107
• /
• 2017

In this study, we analyzed the determinants of wages of college graduates by using the data of "2014 Graduates Occupational Mobility Survey" conducted by Korea Employment Information Service. In general, wages contain two complex pieces of information about whether an individual is employed and the size of the wage. However, in many previous researches on wage determinants, sample selection bias tends to be generated by performing linear regression analysis using only information on wage size. We used the Heckman sample selection models for analysis to overcome this problem. The main results are summarized as follows. First, the validity of the Heckman's sample selection model is statistically significant. Male is significantly higher in both job probability and wage than female. As age increases and parents' income increases, both the probability of employment and the size of wages are higher. Finally, as the university satisfaction increases and the number of certifications acquired increased, both the probability of employment and the wage tends to increase.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.1
• /
• pp.240-259
• /
• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.