• Journal of the Korean Society of Clothing and Textiles
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• v.7 no.1
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• pp.27-35
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• 1983

The purpose of this study is to find out the relationship between needs and factors of clothing Selection. 400 women students from 4 universities and colleges in Seoul were selected and tested. And Hwang Jeongkyu's Needs inventory test sheets for need inventory test and the questionnaire based on Lee Eunju's study for factors of clothing selection were given to the sample. The test data was computerized to get the relationship. The results are as follows: (I) Behavior of clothing selection according to expression of individuality correlated significantly with achievement, aggression, dominance, emotionality, exhibitionism, sex, and autonomy at the .01 level. (2) Behavior of clothing selection according to utility correlated significantly with abasement, and affiliation at the .01 level. and with emotionality at the .05 level, (3) Behavior of clothing selection according to economy correlated significantly with abasement, affiliation, dominance, emotionality, and exhibitionism at the .05 level, and with achievement at the .01 level. (4) Behavior of clothing selection according to modesty correlated significantly with abasement, exhibitionism, and sex at the .01 level. (5) Behavior of clothing selection according to conformity correlated significantly with abasement, emotionality, and autonomy at the .01 level, and with dominance at the .05 level.

• Journal of information and communication convergence engineering
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• v.15 no.1
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• pp.53-61
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• 2017

The preferred feature selection methods for text classification are filter-based. In a common filter-based feature selection scheme, unique scores are assigned to features; then, these features are sorted according to their scores. The last step is to add the top-N features to the feature set. In this paper, we propose an improved global feature selection scheme wherein its last step is modified to obtain a more representative feature set. The proposed method aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in the proposed method to label features according to their discriminative power on classes; these labels are used while producing the feature sets. Experimental results obtained using the well-known 20 Newsgroups and Reuters-21578 datasets with the k-nearest neighbor algorithm and a support vector machine indicate that the proposed method improves the classification performance in terms of a widely known metric ($F_1$).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.12
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• pp.1690-1694
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• 2002

Genetic parameters were estimated for birth weight and weaning weight from three year (1991-1993) data totalling 1100 records of 25 rams to 205 ewes of Indigenous Sabi flock maintained at Grasslands Research Station in Zimbabwe. AIREML procedures were used fitting an Animal Model. The statistical model included the fixed effects of year of lambing, sex of lamb, birth type and the random effect of ewe. Weight of ewe when first joined with ram was included as a covariate. Direct heritability estimates of 0.27 and 0.38, and maternal heritability estimates of 0.24 and 0.09, were obtained for birth weight and weaning weight, respectively. The total heritability estimates were 0.69 and 0.77 for birth weight and weaning weight, respectively. Direct-aternal genetic correlations were high and positive. The corresponding genetic covariance estimates between direct and maternal effects were positive and low, 0.25 and 0.18 for birth weight and weaning weight, respectively. Responses to selection were 0.8 kg and 0.14 kg for birth weight and weaning weight, respectively. The estimated expected correlated response to selection for birth weight by directly selecting for weaning weight was 0.26. Direct heritabilities were moderate; as a result selection for any of these traits should be successful. Maternal heritabilities were low for weaning weight and should have less effect on selection response. Indirect selection can give lower response than direct selection.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.131-139
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• 2013

Support vector machines(SVM) may perform poorly in the presence of noise variables; in addition, it is difficult to identify the importance of each variable in the resulting classifier. A feature selection can improve the interpretability and the accuracy of SVM. Most existing studies concern feature selection in the linear SVM through penalty functions yielding sparse solutions. Note that one usually adopts nonlinear kernels for the accuracy of classification in practice. Hence feature selection is still desirable for nonlinear SVMs. In this paper, we compare the performances of nonlinear feature selection methods such as component selection and smoothing operator(COSSO) and kernel iterative feature extraction(KNIFE) on simulated and real data sets.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.10-21
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• 2017

The purposes of this study were to verify the effects of HMR (Home Meal Replacement) selection attributes on satisfaction and loyalty for HMR. In addition, the moderating role of customer value was examined among selection attributes of HMR, satisfaction and loyalty for HMR. Using a convenience sampling method, the data were collected from those who have bought HMR in Seoul and Kyonggi area. After a total of 235 responses were collected, 220 were used for the analyses. The multiple regression analyses were conducted to test the hypotheses. The results are as follows. First, it was found that product practicality and cooking convenience of HMR selection attributes had an effect on satisfaction of HMR but that ingredients safety and package & circulation period did not have an effect on satisfaction of HMR. Second, satisfaction of HMR significantly impacted loyalty for HMR. Third, in low group for customer value, product practicality of HMR selection attributes had an positive effect on satisfaction of HMR, and ingredients safety of HMR selection attributes had an negative effect on satisfaction of HMR. In high group for customer value, cooking convenience of HMR selection attributes had an positive effect on satisfaction of HMR. In low group as high group for customer value, satisfaction of HMR had a greater impact on loyalty for HMR.

• Journal of Distribution Science
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• v.19 no.12
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• pp.45-56
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• 2021

Purpose: This study aims to introduce a hybrid MCDM model to support the selection of retail store location. Research design, data, and methodology: The hybrid approach of ANP and TOPSIS was used to address the location selection problem. The ANP technique was employed to compute the weights of the selection criteria, whilst the TOPSIS was used to rank alternatives. The proposed approach was then applied into a fashion company in Vietnam to select the best alternatives to be the retail store. Results: The results showed that Candidate 1 - Hai Ba Trung street is the most appropriate selection for locating retail stores. Conclusions: The proposed approach provides the decision makers with more useful methods than traditional ones. Therefore, the model can be applied to the location selection in all industries. In terms of academic contribution, the selection criteria proposed in the research can devote to the literature in the selection of location along with the concept of distribution channels. Additionally, the research also provides insight and guidelines for firms in making decision on retail store location based on limited resources to avoid the waste of funds. However, the results only answer to the context of Vietnam - a developing country. Thus, future research may be extended to developed countries where have better conditions.

• Journal of Distribution Science
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• v.19 no.8
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• pp.25-35
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• 2021

Purpose: This study aims to propose an integrated MCDM model to support the qualified personnel selection in the distribution science. Research design, data, and methodology: The integrated approach of AHP and TOPSIS was employed to address the personnel selection problem. The AHP method was used to define the weights of the selection criteria, whereas the TOPSIS was applied to rank alternatives. The proposed model was then applied into a leading logistics company to select the best alternatives to be the sales deputy manager. Results: The results showed that Candidate 3 is the most qualified personnel for the sales deputy manager position as he is ranked first in the order of preference for recruitment. Conclusions: The proposed model provides the decision makers with more effective and time-saving methods than conventional ones. Therefore, the model can be applied to personnel selection around the world. In terms of theoretical contribution, this study proposes a personnel selection model for choosing the most appropriate candidates. In addition, the study adds to the theory of human resources management and logistics management the full set of personnel selection criteria including education, experience, skills, health, personality traits and foreign language.

• The Research Journal of the Costume Culture
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• v.27 no.4
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• pp.323-335
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• 2019

Identifying the selection criteria of wedding planners, who are the main human resource that contribute to the overall satisfaction level of a wedding's preparation, can produce important data for the wedding industry as it faces a transition period. To develop a scale for wedding planner selection criteria, preliminary items were prepared based on in-depth interviews with an experienced wedding planner group and a comprehensive review of previous studies, such as studies on selection criteria in the wedding industry and service work similar to wedding planners and wedding planner selection criteria. The preliminary items were tested for content validity using a focus group interview, comprising people in the wedding industry, and were refined accordingly. Then, the definitive quantitative questionnaire items were developed after conducting a preliminary survey. The main survey for this study was conducted using the responses of 295 consumers taking an Internet questionnaire. Exploratory factor analysis, Cronbach's alpha, and confirmatory factor analysis were adopted to develop the scale, which was tested for convergent validity and discriminant validity. This study is academically significant in that it developed a selection criteria scale tailored to wedding planners to address an area that has not been covered in previous studies. It is recommended that future studies conduct empirical analyses using the selection criteria developed in this study to compare influential variables that could affect behavior intention.

• The Korean Journal of Food & Health Convergence
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• v.7 no.4
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• pp.1-11
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• 2021

The purpose of this study is to investigate the ramen selection attributes of consumers. This research assigned taste, price, quantity, design, and brand as selection attributes, all of which have already been verified by previous studies as selection attributes when purchasing processed foods. A total of 500 questionnaires were issued, and the survey results were analysed to ensure validity and reliability. A Structural Equation Model was used to test the hypotheses of the study. Based on the analysis, taste, price, quantity, design, and brand had a statistically significant effect on satisfaction. Furthermore, satisfaction had a statistically significant effect on repurchase intention. Among the selection attributes (taste, price, quantity, design, and brand), only price had a statistically significant effect on repurchase intention. However, the influence of the selection attributes on satisfaction varied depending on the consumer's consumption value. In order to analyse the moderating effect of consumption value, the respondent group was divided into a hedonism group and pragmatism group, and analysed. It empirically proved that the hedonistic value-oriented group valued taste, and the practical value-oriented group valued price the most. This study empirically verified the relationship between ramen selection attributes and consumption value, and provided corresponding theoretical and practical implications.