• 유통과학연구
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• 제19권8호
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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.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.575-582
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• 2019

The problem of selecting variables in the presence of outliers is considered. Variable selection and outlier detection are not separable problems because each observation affects the fitted regression equation differently and has a different influence on each variable. We suggest a simultaneous method for variable selection and outlier detection in a linear regression model. The suggested procedure uses a sequential method to detect outliers and uses all possible subset regressions for model selections. A simplified version of the procedure is also proposed to reduce the computational burden. The procedures are compared to other variable selection methods using real data sets known to contain outliers. Examples show that the proposed procedures are effective and superior to robust algorithms in selecting the best model.

• Korean Journal of Acupuncture
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• 제23권4호
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• pp.49-57
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• 2006

Objectives : The aim of this study was to show the rationale of point-selection on the methods of acupuncture and moxibustion in the Shin section(in the Naegyeong chapter) of the Donguibogam. Methods : First, We summarized the cause of each disease in the Shin section(in the Naegyeong chapter) of the Donguibogam. Then, We explained the rationale of acupuncture point-selection referring to the cause of disease, physiology of the Oriental medicine, exposition of acupuncture point name, character of each acupuncture points, flow of meridian pathways and specific acupuncture points etc. Results and Conclusions : Total 44 acupuncture points were used in the Shin section(in the Naegyeong chapter) of the Donguibogam. Most of acupuncture points were specific acupuncture points. but, some rationale of acupuncture point-selection were explained by the cause of disease, physiology of the Oriental medicine, exposition of acupuncture point name, flow of meridian pathways etc.

• Communications for Statistical Applications and Methods
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• 제4권1호
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• pp.11-19
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• 1997

One of the most important issues in regression is variable selection problem. Recently several methods have been proposed to overcome the overparameterization property of Mallow's $C_p$. In this paper we compare these model selection criteria in view of the performance of selecting true model by simulation study.

• 한국통신학회논문지
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• 제21권7호
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• pp.1642-1653
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• 1996

This paper covers feature extraction for automatic handwritten signature verification. Several major feature selection techniques are investigated from a practical perspective to realise an optimal signature verification system, and customised feature set selection based on set-on-set distance measurement is presented. The experimental results have proved the proposed methods to be efficient, offering considerably improved verification performance compared to conventional methods. Also, they dramatically reduce the processing complexity in the verification system.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.617-631
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• 2000

This paper is concerned with suggesting a Bayesian method for variable selection in generalized logit model. It is based on Laplace-Metropolis algorithm intended to propose a simple method for estimating the marginal likelihood of the model. The algorithm then leads to a criterion for the selection of variables. The criterion is to find a subset of variables that maximizes the marginal likelihood of the model and it is seen to be a Bayes rule in a sense that it minimizes the risk of the variable selection under 0-1 loss function. Based upon two examples, the suggested method is illustrated and compared with existing frequentist methods.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1513-1521
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• 2015

Selecting relevant variables for a statistical model is very important in regression analysis. Recently, variable selection methods using a penalized likelihood have been widely studied in various regression models. The main advantage of these methods is that they select important variables and estimate the regression coefficients of the covariates, simultaneously. In this paper, we propose a simple procedure based on a penalized h-likelihood (HL) for variable selection in Poisson hierarchical generalized linear models (HGLMs) for correlated count data. For this we consider three penalty functions (LASSO, SCAD and HL), and derive the corresponding variable-selection procedures. The proposed method is illustrated using a practical example.

• 한국컴퓨터정보학회논문지
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• 제23권9호
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

• 한국인터넷방송통신학회논문지
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• 제16권3호
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• pp.65-71
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• 2016

무선 페이딩 채널에서 시스템의 성능 저하를 효과적으로 개선하기 위하여 협동 릴레이 시스템에 관한 연구가 활발히 진행되고 있다. 그동안의 연구는 고정된 릴레이를 가정하였는데, 최근에는 실제 이동통신 환경을 고려하여 공간적으로 랜덤하게 분포된 릴레이를 갖는 협동 릴레이 시스템의 연구가 시작되고 있다. 특히 통신 자원의 효율이 높은 추가 기회전송 협동 릴레이 시스템에서는 릴레이를 선택하는 방법에 따라서 시스템의 성능이 변화하는데, 이에 대한 비교 연구는 발표되지 않았다. 따라서 본 논문에서는 최대 SNR 선택법과 최대-최소 SNR 선택법을 대상으로 최대비 결합을 사용한 추가 기회전송 협동 릴레이 시스템의 성능을 유도하였고, 송신전력이 소스와 릴레이로 배분되었을 때의 오수신율을 비교하였다. 본 논문의 해석적 결과는 이동통신 시스템에 기회전송 증가 릴레이 시스템을 도입하였을 경우 릴레이를 선택하고 송신 전력을 배분하는데 이론적 근거로 활용할 수 있을 것으로 생각된다.

• Structural Monitoring and Maintenance
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• 제4권1호
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• pp.85-105
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• 2017

Monitoring is the most important part of the construction and operation of the embankment dams. Applied instruments in these dams should be determined based on dam requirements and specifications. Instruments selection considered as one of the most important steps of monitoring plan. Competent instruments selection for dams is very important, as inappropriate selection causes irreparable loss in critical condition. Lack of a systematic method for determining instruments has been considered as a problem for creating an efficient selection. Nowadays, decision making methods have been used widely in different sciences for optimal determination and selection. In this study, the Multi-Attribute Decision Making is applied by considering 9 criteria and categorisation of 8 groups of geotechnical instruments. Therefore, the Analytic Hierarchy Process and Multi-Criteria Optimisation and Compromise Solution methods are employed in order to determine the attributes' importance weights and to prioritise of instruments for embankment dams, respectively. This framework was applied for a rock fill with clay core dam. The results indicated that group decision making optimizes the selection and prioritisation of monitoring instruments for embankment dams, and selected instruments are reliable based on the dam specifications.