• Communications for Statistical Applications and Methods
• /
• 제29권6호
• /
• pp.629-640
• /
• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• 한국정밀공학회지
• /
• 제16권3호통권96호
• /
• pp.215-221
• /
• 1999

The object of the thermal error compensation system in machine tools is improving the accuracy of a machine tool through real time error compensation. The accuracy of the machine tool totally depends on the accuracy of thermal error model. A thermal error model can be obtained by appropriate combination of temperature variables. The proposed method for optimal variable selection in the thermal error model is based on correlation grouping and successive regression analysis. Collinearity matter is improved with the correlation grouping and the judgment function which minimizes residual mean square is used. The linear model is more robust against measurement noises than an engineering judgement model that includes the higher order terms of variables. The proposed method is more effective for the applications in real time error compensation because of the reduction in computational time, sufficient model accuracy, and the robustness.

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

• Communications for Statistical Applications and Methods
• /
• 제24권6호
• /
• pp.673-683
• /
• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• Journal of the Korean Data and Information Science Society
• /
• 제24권1호
• /
• pp.201-209
• /
• 2013

For censored regression, it is often the case that some input variables are not important, while some input variables are more important than others. We propose a novel algorithm for selecting such important input variables for censored kernel regression, which is based on the penalized regression with the weighted quadratic loss function for the censored data, where the weight is computed from the empirical survival function of the censoring variable. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of important input variables. Experimental results are then presented which indicate the performance of the proposed variable selection method.

• 대한전기학회논문지:전력기술부문A
• /
• 제48권6호
• /
• pp.683-691
• /
• 1999

In this paper, it is suggested that the selection method of optimal parameter of power system stabilizer(PSS) with robustness in low frequency oscillation for power system using Real Variable Elitism Genetc Algorithm(RVEGA). The optimal parameters were selected in the case of power system stabilizer with one lead compensator, and two lead compensator. Also, the frequency responses characteristic of PSS, the system eigenvalues criterion and the dynamic characteristic were considered in the normal load and the heavy load, which proved usefulness of RVEGA compare with Yu's compensator design theory.

• 품질경영학회지
• /
• 제29권1호
• /
• pp.11-23
• /
• 2001

This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

• Journal of the Korean Data and Information Science Society
• /
• 제22권4호
• /
• pp.795-801
• /
• 2011

In machine learning and statistics it is often the case that some variables are not important, while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables in the kernel Cox regression. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of relevant variables in the kernel Cox regression. Experimental results are then presented which indicate the performance of the proposed method.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 1998년도 추계학술대회 논문집
• /
• pp.560-567
• /
• 1998

The pitch of spot-weld is a important variable in a view of both production cost and strength of car-body. Various renditions for the selection of pitches have been researched and especially in this paper the buckling analysis is carried out for the 2-sheet pannel structures. The optimal pitch is obtained by optimization program and FEM, which can enhance the buckling strength.

• 한국음향학회지
• /
• 제18권7호
• /
• pp.92-100
• /
• 1999

마이크로셀 이동 통신에서 기지국으로부터 이동국까지의 전파환경을 보다 신속하고 정확히 해석하기 위해 전파의 반사횟수와 전파경로를 연산처리 할 수 있는 삼각해석법 알고리즘을 제안한다. 그리고, 이동국이 가시거리 영역의 전파 음영지역 또는 가시거리 영역과 임의의 경사각으로 기울어져 있는 비가시거리 영역의 전파 음영지역에 위치한 경우를 각각 가정하고 제안 알고리즘을 시뮬레이션하여, 그 결과를 분석함으로써 마이크로셀 이동 통신의 최적 기지국 위치 선정 조건을 제시한다.