• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.537-552
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• 2019

Purpose: The development of information technology makes it easy to utilize high-dimensional categorical data. In this regard, the purpose of this study is to propose a novel method to select the proper categorical variables in high-dimensional categorical data. Methods: The proposed feature selection method consists of three steps: (1) The first step defines the goodness-to-pick measure. In this paper, a categorical variable is relevant if it has relationships among other variables. According to the above definition of relevant variables, the goodness-to-pick measure calculates the normalized conditional entropy with other variables. (2) The second step finds the relevant feature subset from the original variables set. This step decides whether a variable is relevant or not. (3) The third step eliminates redundancy variables from the relevant feature subset. Results: Our experimental results showed that the proposed feature selection method generally yielded better classification performance than without feature selection in high-dimensional categorical data, especially as the number of irrelevant categorical variables increase. Besides, as the number of irrelevant categorical variables that have imbalanced categorical values is increasing, the difference in accuracy between the proposed method and the existing methods being compared increases. Conclusion: According to experimental results, we confirmed that the proposed method makes it possible to consistently produce high classification accuracy rates in high-dimensional categorical data. Therefore, the proposed method is promising to be used effectively in high-dimensional situation.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.795-801
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• 2011

In machine learning and statistics it is often the case that some variables are not important, while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables in the kernel Cox regression. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of relevant variables in the kernel Cox regression. Experimental results are then presented which indicate the performance of the proposed method.

• Korean Journal of Child Studies
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• v.20 no.4
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• pp.195-211
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• 1999

This study investigated developmental trends and sex differences in the relation between children's self-esteem and relevant variables by proposing and testing the causal model. The 763 children who participated in the study were 3rd, 5th, and 7th grade students. Major findings were that physical appearance was the most powerful determinant of self-esteem. Students with high self-esteem were more learning oriented, used more motivational behaviors and had higher academic achievement. The findings from this analysis of the causal model revealed remarkable developmental differentiation and stability.

• Journal of Distribution Science
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• v.21 no.5
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• pp.63-73
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• 2023

Purpose: This study proposes a model for future research agendas on knowledge management activities as a source of increasing sustainable competitive advantage. Based on the literature, the role of knowledge management in sustainable competitive advantage does not necessarily have a significant effect but involves other variables. For this reason, future research proposals on the role of knowledge management on sustainable competitive advantage and other relevant variables need to be carried out. Research design, data, and methodology: This research uses a literature study. The model development stage is (1) relevant research studies, (2) identification of relevant theories and related variables, (3) developing and making a hypothesis (4) proposed model. Results: From the literature study, it was found that knowledge management plays a role in increasing managerial competence, market orientation, and innovation. Furthermore, managerial competence, market orientation, and innovation significantly affect sustainable competitive advantage. However, other studies have found a different relationship. Conclusions: This study proposes a research model on the role of knowledge management, managerial competence, market orientation, and innovation to improve sustainable competitive advantage. The study results can be used for further research based on the proposed model and as a reference for company owners and management to increase competitive advantage.

• Korean Journal of Child Studies
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• v.22 no.1
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• pp.227-240
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• 2001

This review examined trends in research on the assessment of Korean children's story comprehension. Specific areas that were analyzed included types of questions asked in the assessment and variables treated as relevant to the assessment of story comprehension. Literal, inferential, and critical questions were identified. Among the 33 studies reviewed, 20 used all 3 types of questions, the other 13 used only literal questions. The studies using only literal questions interpreted comprehension of a story as "comprehension of letters and/or components of a story." The other studies interpreted it as "comprehension of implied meaning of a story." Other relevant variables were "teaching strategies" (29 studies) and "structural components" (4 studies). None of the studies treated "children's internal variables related to story comprehension."

• Journal of the Korean Society of Clothing and Textiles
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• v.23 no.2
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• pp.262-271
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• 1999

This paper presents a conceptual mode of the clothing complaining behavior process following dissatisfaction in a retail environment and influence of relevant variables. The data were collected 250 male and 358 female consumers by questionnaire employing critical incident technique. Given dissatisfaction with clothing the complaining behavior undertaken will be largely dependent on product importance the likelihood of success one's attitude toward complaining and demorgraphic variables. Through empirical research the clothing complaining behavior was dependent on the likelihood of success sex, dimension of complaining cost and product importance, Brand satisfaction was affected by only perceived justice. And repurchasing behavior was dependent upon brand satisfaction education product importance and income.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.507-521
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• 2021

Detecting outliers among high-dimensional data encounters a challenging problem of screening the variables since relevant information is often contained in only a few of the variables. Otherwise, when a number of irrelevant variables are included in the data, the distances between all observations tend to become similar which leads to making the degree of outlierness of all observations alike. The subspace outlier detection method overcomes the problem by measuring the degree of outlierness of the observation based on the relevant subsets of the entire variables. In this paper, we survey recent subspace outlier detection techniques, classifying them into three major types according to the subspace selection method. And we summarize the techniques of each type based on how to select the relevant subspaces and how to measure the degree of outlierness. In addition, we introduce some computing tools for implementing the subspace outlier detection techniques and present results from the simulation study and real data analysis.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.756-765
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• 2016

In order to investigate variables related to the life satisfaction of women from the multi-cultural families, this study collected relevant studies and applied meta-analysis. As a result, it was found, first, their life satisfaction was between the small effect size and the moderate effect size, on the basis of the relevant variables and Cohen's standards(1992). Second, of the sub variables of their individual characteristic variable, Korean proficiency and nationality acquisition were found to be the small effect size. Third, of the sub variables of their psychological variable and multiple culture-related variable, the effect sizes of self-esteem and the receptive attitude towards multiple cultures were found to be between the moderate effect size and the large effect size. Fourth, the sub variables of their social support variable were found to be generally highly correlated with their life satisfaction. Fifth, the sub variables of daily life and spouse-related variables showed very low levels of effect size overall. This study suggested conclusions and limitations of the research based on the results of the above analyses.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.659-672
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• 2005

This paper evaluates discretization of continuous variables to select relevant variables for supervised learning using mutual information. Three discretization methods, MDL, Histogram and 4-Intervals are considered. The process of discretization and variable subset selection is evaluated according to the classification accuracies with the 6 real data sets of UCI databases. Results show that 4-Interval discretization method based on quantiles, is robust and efficient for variable selection process. We also visually evaluate the appropriateness of the selected subset of variables.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.959-965
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• 2010

For multiclassification, it is often the case that some variables are not important while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables for multiclassification. This algorithm is base on multiclass least squares support vector machine (LS-SVM), which uses results of multiclass LS-SVM using one-vs-all method. Experimental results are then presented which indicate the performance of the proposed method.