• The Research Journal of the Costume Culture
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• v.15 no.5
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• pp.888-901
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• 2007

The purpose of this study was to examine the differences in clothing selection criteria and store selection criteria between each consumer groups by the preference for fast fashion. The data was collected from a questionnaire conducted on 317 female adults. The results were as follows. First, the factor analysis used to identify clothing selection criteria involved the following four factors: fashion, quality/practicality, status symbol, and economics. The consumer group preferring fast-fashion regarded fashion and economics as important factors and the non-preferring group regarded status symbol factor more. Second, five factors(atmosphere, product service/salesperson, shopping convenience, promotion/facilities) of store selection criteria were constructed by factor analysis, The consumer group preferring fast-fashion regarded atmosphere and product as important factors and the non-preferring group considered service/salesperson and promotion/facilities factors more. Third, the consumers who were of low age, low education, low income and unmarried preferred fast fashion brand.

• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1544-1548
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• 2011

A process of choosing a subset of original features, so called feature selection, is considered as a crucial preprocessing step to image processing applications. There are already large pools of techniques developed for machine learning and data mining fields. In this paper, basically two methods, non-feature selection and feature selection, are investigated to compare their predictive effectiveness of classification. Color co-occurrence feature is used for defining image features. Standard Sequential Forward Selection algorithm are used for feature selection to identify relevant features and redundancy among relevant features. Four color spaces, RGB, YCbCr, HSV, and Gaussian space are considered for computing color co-occurrence features. Gray-level image feature is also considered for the performance comparison reasons. The experimental results are presented.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.7
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• pp.883-887
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• 2000

One way of evaluating the effectiveness of a dairy breeding program is to measure response to selection. This may be direct or indirect. The objectives of this study were to estimate expected progress for direct selection on milk, fat and protein yields; to estimate the expected correlated responses on indirect selection for milk, fat and protein yields in Zimbabwean Holstein cattle and to establish the effect of selection intensity on responses. The Animal Model contained fixed effects of herd, year of calving, calving month, dry period, milking frequency and additive effects pertaining to cows, sires and dams. AIREML software package was used to analyse the data. The genetic and phenotypic parameters obtained in this study were used to compute direct and correlated responses to selection. Because of the higher heritabilities in first parity, genetic progress was found to be greater when selection was practised on first parity cows as compared to later lactations. It is therefore recommended that older cows in the herd be replaced with improved heifers so as to enhance genetic progress.

• The International Journal of Costume Culture
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• v.5 no.1
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• pp.26-33
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• 2002

Re purposes of this study were to determine consumers' selection criteria of fashion shopping area and to examine the effects of clothing consumption value and fashion leadership on the selection criteria of fashion shopping area. In addition, the third purpose was to clarify the relation beかeon the above variables and shopping intent. The data were collected from 198 females in their twenties using questionnaire. The results could be summarized as follows. First, the selection criteria of fashion shopping area were composed of 5 factors: scale & variety of shopping area, convenience of location, entertainment, physical service, and atmosphere. Second, the fashion leadership and clothing consumption value except functional and social values had an effect on the selection criteria fashion shopping area. Particularly, the attractiveness-sought value best explained the selection criteria. Third, the fashion leadership and selection criteria fashion shopping area had an effect on the shopping intent. The effect clothing consumption value was not significant.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.743-758
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• 2005

This paper consider penalized likelihood regression with data from exponential family. The fast computation method applied to Gaussian data(Kim and Gu, 2004) is extended to non Gaussian data through asymptotically efficient low dimensional approximations and corresponding algorithm is proposed. Also smoothing parameter selection is explored for various exponential families, which extends the existing cross validation method of Xiang and Wahba evaluated only with Bernoulli data.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.861-870
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• 2005

The Bayesian model selection procedures for the shape parameter of gamma distribution are proposed in order to test that the failure rate of gamma distribution is constant, increasing or decreasing. The encompassing intrinsic Bayes factor by Beger and Pericchi (1996) based on Jeffreys prior for shape parameter is used to investigate the usefulness of the proposed Bayesian model selection procedures via both real data and pseudo data.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.605-616
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• 2004

In this paper we consider the proportional hazard models for survival analysis in the microarray data. For a given vector of response values and gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1149-1157
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• 2013

In this study we compare academic achievements of college students admitted by admissions officer selection with those admitted by general selection. Two measurements of the academic achievements considered are GPA (grade point average) and relative ascending rank of GPA. By the comparison of the academic achievements, we would like to assess the effectiveness of the admissions office selection and then provide a basis for screening good students by that selection. The results of data analysis indicate that the academic achievements of admissions officer selection students tend to be lower than those of early general admission students and also those of regular general admission students tend to be higher than those of early general admission students.

• Journal of Information Technology Applications and Management
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• v.28 no.6
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• pp.97-116
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• 2021

Due to the decrease in the school-age population and government regulations, universities have made great efforts to secure their own competitiveness. In particular, the selection of universities with financial support based on the recent evaluation of the Ministry of Education has become a major concern enough to affect the existence of the university itself. This paper extracts three-year data from 124 major private universities nationwide, and quantitatively analyzes the variables of major universities selected as self-improvement universities, competency reinforcement universities, and universities with limited financial support. As a result of estimating the selection of self-powered universities using the ordered logit model by hierarchically inputting 12 variables, student competitiveness in the metropolitan area (1.318^**), Educational Restitution Rate (4.078^***), University operation expenditure index rate (1.088^***) values were found. Significant positive coefficient values were found in the admission enrollment rate (45.98^***) and the enrollment rate (13.25^***). As a result of analyzing the marginal effects, the increase in the rate of reduction of education costs has always been positive in the selection of self-powered universities, but it was observed that the rate of increase decreases in areas of increase of 150% or more. On the contrary, the probability of becoming a Em-powered university was negative in all sectors, but on the contrary, it was analyzed that marginal effects increased at the same time point. On the other hand, the employment rate of graduates was not able to find direct significance with the result of the selection of Self powered universities. Through this paper, it is expected that each university will analyze the possibility and shortcomings of the selection of Self powered universities in policy making, and in particular, the risk of dropout of selection for the vulnerable field can be predicted using marginal effects. It can be used as major research data for both university evaluators, university officials and students.