• Journal of the Korean Statistical Society
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• 제29권3호
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• pp.307-313
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• 2000

Suppose we have a set of data X1, ···, Xn and employ kernel density estimator to estimate the marginal density of X. in this article bandwith selection problem for kernel density estimator is examined closely. In particular the Kullback-Leibler method (a bandwith selection methods based on average square error (ASE)) is considered.

• Journal of the Korean Statistical Society
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• 제21권1호
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• pp.1-13
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• 1992

This article deals with variable selection problem under a newly formed predictive heteroscedastic discriminant rule that accounts for mulitple homogeneous covariance matrices across the K multivariate normal populations. A general version of predictive discriminant rule, a variable selection criterion, and a criterion for stopping with further selection are suggested. In a simulation study the practical utilities of those considered are demonstrated.

• 대한가정학회지
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• 제31권2호
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• pp.69-80
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• 1993

This study intended to review the historical mate selection process and to present wholesome mate selection process. For this purpose, reviewing the contents of secondary sources and novel literature. The following conclusions were drawn from this study: Mate selection process was a kind of culture which was influenced by social atmosphere. And pre-17th century, most of the young people had social intercourse liberally. Therefore we must constitute wholesome mate selection culture through early socialization about heterosexual social intercourse.

• 대한안전경영과학회지
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• 제7권4호
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• pp.165-177
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• 2005

Supply Chain Management(SCM) system is a critical investment that can affect future competitiveness and performance of a company. Selection of a right SCM system is one of the critical issues. This paper provides the characteristic factors of SCM system selection and the SCM system evaluation and selection model based on Analytic Hierarchy Process(AHP). The proposed model can systematically construct the objectives of SCM system selection to support the business goals. A empirical example demonstrates the feasibility of the proposed model and the model can help a company to make better decision-making in selecting SCM system.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1397-1409
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• 2017

For text categorization task, distinctive text features selection is important due to feature space high dimensionality. It is important to decrease the feature space dimension to decrease processing time and increase accuracy. In the current study, for text categorization task, we introduce a novel statistical feature selection approach. This approach measures the term distribution in all collection documents, the term distribution in a certain category and the term distribution in a certain class relative to other classes. The proposed method results show its superiority over the traditional feature selection methods.

• 수산해양기술연구
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• 제30권3호
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• pp.161-171
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• 1994

Mesh selection analysis for the trawl net were carried out at the Southern Korean Sea and the East China Sea during the 1992-1994 years by the training ship Seabada of the National Fisheries University of Pusan, using A type (51.2mm), B type (70.2mm), C type (77.6mm), D type (88.0mm) square mesh cod-ends. The fishing trials were made using bottom trawl of the trouser type cod-end with cover net. Selection curves and selection parameters were calculated by a logistic model for Sphyraena pinguis, Pampus argenteus, Trachurus japonicus, Pagrus major, Callanthias japonicus, Trichiurus lepturus. The results obtained are summarized as follows: 1. Red barracuda : Selection range and fifty percent selection length in the A type was 115.8mm, 292.8mm, respectively. 2. Harvest fish : Each selection range and fifty percent selection length in the B. C. and D type was 37.7mm, 113.8mm : 40.1mm, 131.7mm and 64.8mm, 148.6mm, respectively. Selection parameters of master curve were : slope, 3.81 : intercept, -6.4. Optimum mesh size was 89.3mm. 3. Horse mackerel : Each selection range and fifty percent selection length in the A, B, C and D type was 43.0mm, 148.3mm : 60.7mm, 183.2mm, 214.5mm and 91.4mm, 254.9mm, respectively. Selection parameters of master curve were : slope 2.30 : intercept, -6.4. Optimum mesh size was 66.8mm. 4. Red seabrem : Selection range and fifty percent selection length in the D type was 42.7mm, 203.4mm, respectively. 5. Yellowsail red bass : Selection range and fifty percent selection length in the A type was 84.0mm, 110.6mm, respectively. 6. Hair tail : Each selection range and fifty percent selection length in the A, B and C type was 59.7mm, 176.0mm : 100.9mm, 250.7mm and 178.6mm, 307.0mm, respectively. Selection parameters of master curve were : slope, 1.54 : intercept, -5.4. Optimum mesh size was 57.5mm.

• 한국도서관정보학회지
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• 제43권3호
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• pp.457-479
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• 2012

본 연구에서는 현재 국내 공공도서관 자료선정업무의 현황을 자료구입비 규모, 자료선정정책과 자료선정기준, 자료선정 담당 인력과 자료선정위원회, 자료선정방법과 자료선정도구 등의 카테고리로 나누어 살펴 본 다음 사서들의 자료선정업무에 대한 인식을 조사하였다. 이러한 현황을 바탕으로 효율적인 자료선정이 이루어질 수 있는 방안을 자료선정정책, 자료선정인력, 자료선정방법의 세 가지 측면으로 나누어 논의하였다. 자료선정업무현황 분석을 위한 데이터 수집은 국내 공립공공도서관 315개관을 대상으로 한 설문조사를 통하여 이루어졌다.

• 대한안전경영과학회지
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• 제9권2호
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• pp.195-214
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• 2007

The purpose of this study is to explore the applicability of AHP(Analytic Hierarchy Process) to select more productive projects among various proposed projects in a particular company. To achieve this research objective, the characteristics of project evaluation and selection are first reviewed with respect to when, where, and how the decision is made. Then the theoretical basis of the AHP is briefly reviewed along with its mathematical underpinnings to construct the framework of project evaluation and selection. To be more specific, the evaluation and selection criteria were reorganized in the AHP-based framework to make the process of project evaluation and selection more productive. Project evaluation and selection is one of the most important activities for the most companies to be more advantageous in the market. Despite the importance of decision making process of project selection, not many of how to choose the best project were suggested as the reliable project selection methods in the industries. It may be because it involves various activities related to conflict resolution among different evaluation criteria, high uncertainties of market, and the unclear tradeoff among various project objectives. Furthermore, the decision, once made at this point, tends to be irrevocable until the whole process turns out to be a complete success or failure. As the result, the AHP method showed better financial performance rather than the traditional method in a case study.

• 스마트미디어저널
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• 제12권3호
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• 응용통계연구
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• 제24권6호
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• pp.1103-1113
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• 2011

Gene expression data is obtained through many stages of an experiment and errors produced during the process may cause missing values. Due to the distinctness of the data so called 'small n large p', genes have to be selected for statistical analysis, like classification analysis. For this reason, imputation and gene selection are important in a microarray data analysis. In the literature, imputation, gene selection and classification analysis have been studied respectively. However, imputation, gene selection and classification analysis are sequential processing. For this aspect, we compare the performance of classification methods after imputation and gene selection methods are applied to microarray data. Numerical simulations are carried out to evaluate the classification methods that use various combinations of the imputation and gene selection methods.