• Journal of the Korea Fashion and Costume Design Association
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• v.14 no.2
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• pp.185-198
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• 2012

This study aims to find out the influential factors and the relative power of these factors on the clothing purchase decision in internet shopping mall. For this purpose, this study surveyed 500 male and female consumers in their 20's~40's for empirical analysis who have ever purchased clothing through internet shopping malls. Respondents are selected using the convenience sampling through internet survey in August 2011. For statistical analysis descriptive statistics, reliability analysis, factor analysis and regression analysis are carried out using SPSS for Windows 12.0. The results are as follows. First, purchase decision is significantly affected by the expressional/physical properties of the product and product pictures. Second, purchase decision is significantly affected by the usefulness of shopping mall and the importance of purchase. Third, in consumer related factors, product value awareness, reliability of shopping mall, clothing involvement, psychological/economical perceived risks and age affect purchase decision. Fourth, in the regression analysis of the above significant variables on purchase decision, the order of power of these influential factors is expressional/physical properties of product, the importance of purchase, product picture, age and clothing involvement. The results of this study will help internet fashion enterprises to handle the variables of product, shopping mall, and consumer related for enhancing the purchase rate.

• Journal of Technologic Dentistry
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• v.33 no.4
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• pp.427-440
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• 2011

Purpose: The following research analyzes the causes of major selection and career decision of students studying dental technology. It is to be used as basic data for the management of career improvement program. Methods: The survey has been processed to 490 college students studying Dental Technology in D-college. Questionnaire consists of major selection confidence sheet (14 items) and career decision confidence sheet (18 items) and was scored with 5-points per question. The collected data was analyzed by the statistical program: SAS V8 for Windows. To test for significance on each item, p < 0.05 has been decided as a standard. Results: The analysis of result about the level of confidence on major selection has valid difference by genders, serving military service or not, experience of studying one more year to enter the college or not, making career decision and grade. The analysis of result about career decision has valid difference by gender, serving military service, career decision, day and night course, age and native place. Conclusion: We develop the career advice program and manage it effectively, the confidence on the major selection and pride about its faculty will be high to dental technology students.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.4
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• pp.89-101
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• 2015

In this study, it examined whether recognized career barriers among students of majoring in aviation affected career stress and career decision level, and career stress moderated the impact of career barriers on career decision level. For this purpose, it conducted a survey of aviation-related college students in C university from Sep. 21th. 2015 to Oct. 8th. 2015. A total of 225 questionnaires were analyzed using SPSS 21.0 statistical package program and frequency analysis, factor analysis, reliability analysis, regression analysis, and correlation analysis were conducted. Findings indicated that career barriers partly influenced the career stress and career decision level. Also, career stress had an negative effect on career decision level. Lastly, career stress moderated the impact of career barriers on career decision level. The implication of this paper would be used as a fundamental material to guide for career direction to students majoring in aviation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.118-123
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• 2007

In this paper, we propose an improved global soft decision for speech enhancement in noise environments. From an examination of statistical model-based speech enhancement, it is shown that the global soft decision has a fundamental drawback at the offset region of speech signals. To overcome the drawback, we apply a new speech enhancement method based on a smoothed Global likelihood ratio to the global soft decision. Performances of the proposed method are evaluated by subjective tests under various environments and yield better results compared with the reported speech enhancement method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.109-118
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• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.997-1004
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• 2009

A standard approach is to classify all of future observations. In some cases, however, it would be desirable to defer a decision in particular for observations which are hard to classify. That is, it would be better to take more advanced tests rather than to make a decision right away. This motivates a classifier with a reject option that reports a warning for those observations that are hard to classify. In this paper, we present the method which gives efficient computation with a reject option. Some numerical results show strong potential of the propose method.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.449-466
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• 2006

We propose to use variable selection methods based on penalized regression for pruning decision tree ensembles. Pruning methods based on LASSO and SCAD are compared with the cluster pruning method. Comparative studies are performed on some artificial datasets and real datasets. According to the results of comparative studies, the proposed methods based on penalized regression reduce the size of boosting ensembles without decreasing accuracy significantly and have better performance than the cluster pruning method. In terms of classification noise, the proposed pruning methods can mitigate the weakness of AdaBoost to some degree.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.847-859
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• 2011

cDNA microarray-based comparative genomic hybridization(CGH) data includes low-intensity spots and thus a statistical strategy is needed to detect subtle differences between different cancer classes. In this study, genes displaying a high frequency of alteration in one of the different classes were selected among the pre-selected genes that show relatively large variations between genes compared to total variations. Utilizing copy-number changes of the selected genes, this study suggests a statistical approach to predict patients' classes with increased performance by pre-classifying patients with similar genetic alteration scores. Two-stage logistic regression model(TLRM) was suggested to pre-classify homogeneous patients and predict patients' classes for cancer prediction; a decision tree(DT) was combined with logistic regression on the set of informative genes. TLRM was constructed in cDNA microarray-based CGH data from the Cancer Metastasis Research Center(CMRC) at Yonsei University; it predicted the patients' clinical diagnoses with perfect matches (except for one patient among the high-risk and low-risk classified patients where the performance of predictions is critical due to the high sensitivity and specificity requirements for clinical treatments. Accuracy validated by leave-one-out cross-validation(LOOCV) was 83.3% while other classification methods of CART and DT performed as comparisons showed worse performances than TLRM.

• Journal of the Korean Home Economics Association
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• v.20 no.3
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• pp.65-83
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• 1982

In this paper, the writer makes attempts to investigate what kinds of decision-making patterns are adopted, when husbands and wives make up their minds about home managerial problems of farming or fishing villages in Jeju Island. The data in this study were drawn through the questionaire collected from 299 families of farming of fishing villages in Jeju Island. Percentage and F-test applied to statistical analysis. The results are found as follows: 1. Although husbands and wives do joint decision making with each other, some decision making spheres are classified according to problems. 2. Family types are mainly made up of Autonomic family and syncratic family, but there are comparatively by far syncratic family in Jeju Island than any other area. 3. The background variables to be influenced upon are their ages, educational level, duration of marriage, the number of whole family, the number of daughters, the number of children, managing power of their living expenses, satisfaction of their conjugal lives, daily communication status, quarrels between them, locations of farming or fishing villages, etc.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.181-187
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• 2014

Dealing with uncertainty is always a challenging problem. Intuitionistic fuzzy sets was presented to manage situations in which experts have some membership and non-membership value to assess an alternative. Hesitant fuzzy sets was used to handle such situations in which experts hesitate between several possible membership values to assess an alternative. In this paper, the concept of intuitionistic hesitant fuzzy set is introduced to provide computational basis to manage the situations in which experts assess an alternative in possible membership values and non-membership values. Distance measure is defined between any two intuitionistic hesitant fuzzy elements. Fuzzy technique for order preference by similarity to ideal solution is developed for intuitionistic hesitant fuzzy set to solve multi-criteria decision making problem in group decision environment. An example is given to illustrate this technique.