• 한국데이터정보과학회:학술대회논문집
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• 2001.10a
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• pp.76-78
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• 2001

Variable selection in oriental medical research is considered. Decision tree analysis algorithms such as CHAID, CART, C4.5 and QUEST have been successfully applied to a medical research. Paralysis disease is a highly dangerous and murderous disease which accompanied with a great deal of severe physical handicap. In this paper, we explore the use of CHAID algorithm for selecting variables for evidence-diagnosis of paralysis, disease. Empirical results comparing our proposed method to the method using Wilks $\lambda$ given.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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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.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.471-483
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• 2012

One of the most important problems in cluster analysis is the selection of variables that truly define cluster structure, while eliminating noisy variables that mask such structure. Brusco and Cradit (2001) present VS-KM(variable-selection heuristic for K-means clustering) procedure for selecting true variables for K-means clustering based on adjusted Rand index. This procedure starts with the fixed number of clusters in K-means and adds variables sequentially based on an adjusted Rand index. This paper presents an updated procedure combining the VS-KM with the automated K-means procedure provided by Kim (2009). This automated variable selection procedure for K-means clustering calculates the cluster number and initial cluster center whenever new variable is added and adds a variable based on adjusted Rand index. Simulation result indicates that the proposed procedure is very effective at selecting true variables and at eliminating noisy variables. Implemented program using R can be obtained on the website "http://faculty.knou.ac.kr/sskim/nvarkm.r and vnvarkm.r".

• International Journal of Quality Innovation
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• v.4 no.2
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• pp.16-31
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• 2003

A reducing variation of quality characteristics is a typical example of quality improvement. In such a case, we treat the quality characteristic, as a response variable and need to find active factors affecting the response from many candidate factors since reducing the variation of the response will be achieved by reducing variation of the active factors. In this paper, we first derive a method of selecting an active factor by linear regression. It is well known that correlation between factors deteriorates the precision of estimators. We, therefore, examine robustness of the selecting method against the correlation in the data set and derive an evaluation method of the deterioration brought by the correlation. Furthermore, some examples of selecting and evaluation methods are shown to demonstrate practical usage of the methods.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.05a
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• pp.125-134
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• 2000

In this paper, we proposed a new circuit construction method that reduced the number of CMOS devices of singleton fuzzy controller(SFC) through the proposing a new membership function circuit(MFC) which uses the language variable selecting and the coefficient selecting circuit. According to the range of input values, we can choose the language variables beforehand which will be used in the inference. So we proposed the new MFC which generates the only necessary language variables. Also, we removed all rules of which adapting degree of their antecedents is zero through proposing the coefficient selecting circuit which beforehand selects the coefficients which will influence the inference result. Though this method, we simplified the structure of SFC and reduced the size of hardware. And to solve the problem in the current mode with respect to the restriction of the fan-out number, voltage-input and current-out membership function circuits are constituted of operational transconductance amplifiers. A membership function circuit which includes the language variable selecting circuit, a minimum operation circuit we implemented by current mode CMOS devices. As a result of applying proposed method, total numbers of blocks and devices wave decreased. If the number of variables and antecedents are getting larger, this method is more efficient.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.11a
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• pp.357-365
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• 2000

In this paper, we proposed a new circuit construction method that reduced the number of CMOS devices of singleton fuzzy controller(SFC) through the proposing a new membership function circuit(MFC) which uses the language variable selecting and the coefficient selecting circuit. According to the range of input values, we can choose the language variables beforehand which will be used in the inference. So we proposed the new MFC which generates the only necessary language variables. Also, we removed all rules of which adapting degree of their antecedents is zero through proposing the coefficient selecting circuit which beforehand selects the coefficients which will influence the inference result. Though this method, we simplified the structure of SFC and reduced the size of hardware. And to solve the problem in the current mode with respect to the restriction of the fan-out number, voltage-input and current-out membership function circuits are constituted of operational transconductance amplifiers. A membership function circuit which includes the language variable selecting circuit, a minimum operation circuit we implemented by current mode CMOS devices. As a result of applying proposed method, total numbers of blocks and devices wave decreased. If the number of variables and antecedents are getting larger, this method is more efficient.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.267-272
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• 2006

A procedure for selecting regressors in the linear regression model is suggested using local influence approach. Under an appropriate perturbation scheme, the effect of perturbation of regressors on the profile log-likelihood displacement is assessed for variable selection. A numerical example is provided for illustration.

• Journal of Korean Society for Quality Management
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• v.37 no.1
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• pp.29-39
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• 2009

This article investigates economic-statistical design of VSSI(variable sampling size and interval) cause-selecting charts considering two assignable causes. We consider a process which is composed of two dependent sub-processes. In each sub-process, two kinds of assignable cause may exist. We propose a procedure for designing VSSI cause-selecting charts, based on Lorenzen and Vance model. Computational experiments show that the VSSI cause-selecting chart is superior to the FSSI cause-selecting chart in the economic-statistical characteristics, even under two assignable causes.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.407-422
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• 2000

This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.690-691
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• 2006

Variable valve timing unit, which is able to decrease environmental load and improve fuel economy is loaded onto many automobiles recently. This unit consists mainly of sprocket, housing and rotor. These parts are requested different properties according to environment. We produce sintered parts for variable valve timing unit by selecting compact, sinter process and special treatment according to demanded properties. In this paper, demanded properties of sintered parts for variable valve timing unit and adopted technique to satisfy them are presented.