• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.226-236
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• 2021

The purpose of this study is to analyze the type of latent profile for general high school students' subject selection criteria and to identify the characteristics of the latent class. The survey data of 1072 general high school students (male; 648, female; 424) in G city, Jeollabuk-do and the scale composed of 8 sub-factors: 'SAT orientation', 'academic achievement', 'ability orientation', 'pursuit of interest', 'teacher orientation', 'career development', 'others' recommendation', and 'subject availability' were used for latent profile analysis and cross-analysis between potential layers. As a result of the analysis, high school students' perceptions of subject selection were classified into four latent profiles. The four groups were named 'High Perception Type', 'Low Perception Type', 'Self-Directed Type', and 'Stability-Oriented Type' according to their types. It was found that there was a difference between the latent classes in the importance and performance level of the subject selection criteria. These results can help identify the subject selection tendencies of latent groups in the operation of the 2015 revised curriculum and the 2025 high school credit system that emphasizes the student-centered course selection curriculum and they can also provide customized course selection guidance considering individual differences.

• International Journal of Contents
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• v.18 no.3
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• pp.49-57
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• 2022

The purpose of this study was to investigate the satisfaction of personnel selection process according to type of personnel manager and to examine whether the relationship between the type of personnel manager and the satisfaction with the personnel selection process was moderated by the applicant's perception of procedural justice. This study was conducted using a between-group design with 208 students from a four-year university in Korea. One group watched a video in which a human personnel manager selected employees and the other group watched a video in which an AI personnel manager selected employees. Participants were randomly assigned to a condition, responded to a demographic questionnaire, and answered measures of procedural justice and satisfaction with personnel selection after watching the video. As a result, the selection process satisfaction was significantly higher when the human personnel manager conducted the selection process than when the AI personnel manager conducted such process. In addition, when procedural justice was perceived as low, there was a significant difference in satisfaction between human and AI groups. However, when procedural justice was perceived as high, there was no significant difference in satisfaction between the two groups. Based on study results, the significance and limitations of this study and suggestions for future studies are discussed.

• Journal of Distribution Science
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• v.20 no.3
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• pp.1-11
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• 2022

Purpose: This study aims at understanding the impacts of three omnichannel attributes (channel transparency, channel uniformity, channel convenience) and four customer perceptions (perceived innovativeness, perceived personalization, perceived risk, perceived credibility) on customer experience and channel selection decision. Research design and methodology: A quantitative online survey with 356 shoppers was executed. The partial least squares linear structural model (PLS-SEM) and Smart PLS were adopted to analyze the collected data and test the proposed hypotheses. Results: The research findings indicate four dominant results: (i) The customers' channel selection is directly determined by customer experience; perceived innovativeness; perceived personalization; perceived risk; and perceived credibility; and (ii) among these, the perceived risk shows negative impact on the customer's experience and customers' channel selection whereas others reveal the positive status; (iii) The customer experience represents the most decisive impact on the channel selection, then perceived personalization, perceived credibility, perceived innovativeness, and perceived risk. (iv) Three proposed channel attributes (transparency, uniformity, convenience) significantly influence the overall customer experience. Conclusions: This research adds to the body of knowledge in omnichannel retailing, customer experience, and customer channel selection. Furthermore, this research provides omnichannel retailers with practical implications for improving customer channel selection.

• Korean Management Science Review
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• v.12 no.2
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• pp.15-30
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• 1995

The objectives of this paper are to provide software developer selection criteria and to develop evaluation framework using AHP (Analytic Hierarchy Process). The selection criteria are extracted from Software Development Life Cycle, Quality Assurance, and Productivitiy of Organization. In this paper, the selection model is proposed and its examples are illustrated. Though some further research is required, the proposed model can be regarded as a basis of a DSS for the selection of the software developer.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.143-155
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• 2009

Best subset selection procedure based on mutual information (MI) between a set of explanatory variables and a dependent class variable is suggested. Derivation of multivariate MI is based on normal mixtures. Several types of normal mixtures are proposed. Also a best subset selection algorithm is proposed. Four real data sets are employed to demonstrate the efficiency of the proposals.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.553-562
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• 2004

In this short communication we discuss the bias problems of CART in split variable selection and suggest a method to reduce the variable selection bias. Penalties proportional to the number of categories or distinct values are applied to the splitting criteria of CART. The results of empirical comparisons show that the proposed modification of CART reduces the bias in variable selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3945-3964
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• 2015

Since the decentralized structure and the blindness of a large-scale wireless network make it difficult to collect the real-time channel state or other information from random distributed relays, a fundamental question is whether it is feasible to perform the relay selection without this knowledge. In this paper, a Slotted ALOHA based Greedy Relay Selection (SAGRS) scheme is presented. The proposed scheme allows the relays satisfying the user's minimum transmission request to compete for selection by randomly accessing the channel through the slotted ALOHA protocol without the need for the information collection procedure. Moreover, a greedy selection mechanism is introduced with which a user can wait for an even better relay when a suitable one is successfully stored. The optimal access probability of a relay is determined through the utilization of the available relay region, a geographical region consisting of all the relays that satisfy the minimum transmission demand of the user. The average number of the selection slots and the failure probability of the scheme are analyzed in this paper. By simulations, the validation and the effectiveness of the SAGRS scheme are confirmed. With a balance between the selection slots and the instantaneous rate of the selected relay, the proposed scheme outperforms other random access selection schemes.

• Architectural research
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• v.4 no.1
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• pp.45-52
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• 2002

Selection procedures of earth retention systems are increasingly complex and directly related to the serviceability of the retaining structure selection systems since significant changes in earth retention technology motivates the review of design, and selection processes of earth retaining structures. Collection and classification of retaining structure selection knowledge are key issues because two expert groups, geotechnical and structural engineers, are mainly involved in the retaining structure selection. The course of natural tendency of expert knowledge are investigated considering the decision factors. The decision factors for selecting retaining structures are divided into four categories: application of the structure, and spatial, behavior, and economic constraints.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5592-5609
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• 2017

JPEG steganography detection is an active research topic in the field of information hiding due to the wide use of JPEG image in social network, image-sharing websites, and Internet communication, etc. In this paper, a new steganalysis method for content-adaptive JPEG steganography is proposed by integrating the evolutionary feature selection and classifier ensemble selection. First, the whole framework of the proposed steganalysis method is presented and then the characteristic of the proposed method is analyzed. Second, the feature selection method based on genetic algorithm is given and the implement process is described in detail. Third, the method of classifier ensemble selection is proposed based on Pareto evolutionary optimization. The experimental results indicate the proposed steganalysis method can achieve a competitive detection performance by compared with the state-of-the-art steganalysis methods when used for the detection of the latest content-adaptive JPEG steganography algorithms.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.149-161
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• 2019

In this article, we suggest the following approaches to simultaneous variable selection and outlier detection. First, we determine possible candidates for outliers using properties of an intercept estimator in a difference-based regression model, and the information of outliers is reflected in the multiple regression model adding mean shift parameters. Second, we select the best model from the model including the outlier candidates as predictors using stochastic search variable selection. Finally, we evaluate our method using simulations and real data analysis to yield promising results. In addition, we need to develop our method to make robust estimates. We will also to the nonparametric regression model for simultaneous outlier detection and variable selection.