• 과학교육연구지
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• 제39권2호
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• pp.278-289
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• 2015

연구의 목적은 영재교육대상자의 선발 방법이다. 즉, 시험 선발과 자동 진급에 따라 선발된 영재학생간의 학업정서, 메타인지, 자기효능감을 차이를 비교, 분석함으로써 선발 방법에 따른 영재 학생의 특성을 이해하고 효과적인 영재교육을 위한 영재교육대상자 판별 및 선발에 있어서의 타당한 근거를 마련해 보고자 한다. 본 연구의 결과는 다음과 같다. 첫째, 영재선발방법에 따라 선발된 영재 학생간의 학업정서, 메타인지능력 및 자기효능감의 연구에서는 시험 선발 영재학생과 자동 진급 영재 학생 간에는 유의한 차이가 없었다. 영재라는 동질 집단 조건에서는 선발방법의 차이에 따른 영향은 없는 것으로 판단된다. 둘째, 동일한 선발된 방법에 따른 비교에서, 영재학생의 학업정서를 살펴보면, 시험선발의 경우 초등과 중등 모두에서 유의한 차이가 없었으나, 자동진급에서는 중등의 영재교육기관별 분석에서 영재교육원 학생이 영재학급 학생보다 높았음을 알 수 있었다(p<.05). 메타인지능력에서 있어서는 초등에서는 차이가 없었으나, 중등의 경우 시험선발에서 남학생이, 영재교육원 학생이 높았고(p<.05), 자동진급에서는 영재교육원 학생이 높았음을 알았다(p<.05). 또한 자기효능감에서는 선발 방식에 따라 차이가 없었고 단지 자동진급에서 영재교육원 학생이 영재학급학생보다 높았음을 알았다(p<.05).

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

• Genomics & Informatics
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• 제5권3호
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• pp.95-101
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• 2007

In this paper, we consider the variable selection methods in the Cox model when a large number of gene expression levels are involved with survival time. Deciding which genes are associated with survival time has been a challenging problem because of the large number of genes and relatively small sample size (n<

• Communications for Statistical Applications and Methods
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• 제22권2호
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• pp.147-157
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• 2015

We consider a sparse high-dimensional linear regression model. Penalized methods using LASSO or non-convex penalties have been widely used for variable selection and estimation in high-dimensional regression models. In penalized regression, the selection and prediction performances depend on which penalty function is used. For example, it is known that LASSO has a good prediction performance but tends to select more variables than necessary. In this paper, we propose an additive sparse penalty for variable selection using a combination of LASSO and minimax concave penalties (MCP). The proposed penalty is designed for good properties of both LASSO and MCP.We develop an efficient algorithm to compute the proposed estimator by combining a concave convex procedure and coordinate descent algorithm. Numerical studies show that the proposed method has better selection and prediction performances compared to other penalized methods.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Asian-Australasian Journal of Animal Sciences
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• 제20권3호
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• pp.453-460
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• 2007

An overview of developments important in the future of animal breeding is discussed. Examples from the application of quantitative genetic principles to selection in chickens and mice are given. Lessons to be learned from these species are that selection for production traits in livestock must also consider selection for reproduction and other fitness-related traits and inbreeding should be minimized. Short-term selection benefits of best linear unbiased predictor methodology must be weighed against long-term risks of increased rate of inbreeding. Different options have been developed to minimize inbreeding rates while maximizing selection response. Development of molecular genetic methods to search for quantitative trait loci provides the opportunity for incorporating marker-assisted selection and introgression as new tools for increasing efficiency of genetic improvement. Theoretical and computer simulation studies indicate that these methods hold great promise once genotyping costs are reduced to make the technology economically feasible. Cloning and transgenesis are not likely to contribute significantly to genetic improvement of livestock production in the near future.

• 한국사회정책
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• 제20권3호
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• pp.9-46
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• 2013

사회보장법에 나타나는 위임의 정도는 의회의원후보공천방식과 의회상임위원회제도의 운영방식과 관련이 있다. 위임이 적은 사회보장법 구조는 상향식 공천방식과 지속적으로 임기를 보장하는 상임위원회제도와 친화적이다. 독일과 같이 당원이 연방의원후보를 결정하는 과정에서 중요한 역할을 하는 상향식 공천방식과 지속적인 임기를 보장하는 상임위원회제도는 위임이 적은 독일의 사회법과 친화성을 갖는다. 반면에 위임이 많은 사회보장법 구조는 하향식 공천방식과 지속적인 임기를 보장하지 않는 상임위원회제도와 친화적이다. 우리나라와 같이 국회의원후보자를 중앙당에서 주도적으로 결정하는 하향식 공천방식과 지속적인 임기를 보장하지 않는 상임위원회제도는 위임이 많은 우리나라의 사회보장법과 친화성을 갖는다.

• 대한인간공학회지
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• 제24권3호
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• pp.1-10
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• 2005

The primary objective of this study is to design input methods for assisting button selection tasks on a PDA screen. Familiar methods in the existing computing environments were investigated to develop aiding methods. Factors manipulated in the experiment included aiding method, button size, and users' prior experience with PDAs. A total of sixteen participants examined the usability of button selection tasks. Two types of button selection tasks were used as experimental tasks; one was selecting a target button, and the other was selecting multiple target buttons consecutively. The results showed that the aiding method and the button size had significant effects on the subjective satisfaction as well as the performance. In addition, users' prior experience with PDAs affected the performance significantly. The interaction between the aiding method and the button size was found to have significant effects on the performance. However, the interaction effect between the button size and the PDA experience was significant on the task performance time only for the multiple button selection tasks. Design considerations were proposed based on the experimental results. These can be applied to the PDA interaction design to make the PDAs more usable.

• Communications for Statistical Applications and Methods
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• 제18권3호
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• pp.319-331
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• 2011

In this paper, we propose automatic procedures for the model selection of various univariate time series data. Automatic model selection is important, especially in data mining with large number of time series, for example, the number (in thousands) of signals accessing a web server during a specific time period. Several methods have been proposed for automatic model selection of time series. However, most existing methods focus on linear time series models such as exponential smoothing and autoregressive integrated moving average(ARIMA) models. The key feature that distinguishes the proposed procedures from previous approaches is that the former can be used for both linear time series models and nonlinear time series models such as threshold autoregressive(TAR) models and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA-GARCH) models. The proposed methods select a model from among the various models in the prediction error sense. We also provide an R package autots that implements the proposed automatic model selection procedures. In this paper, we illustrate these algorithms with the artificial and real data, and describe the implementation of the autots package for R.

• Journal of the Korean Data and Information Science Society
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• 제21권5호
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• pp.901-908
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• 2010

다중회귀모형에서 변수선택법 중에서 전진선택과 후진제거의 과정을 기하학적으로 표현하는 그래픽적 방법을 제안한다. 반지름이 1인 반원의 제1사분면에는 전진선택 과정을, 제2사분면에는 후진제거 과정을 표현한다. 각 단계에서 회귀제곱합을 벡터로 표현하고, 추가제곱합 또는 부분결정계수를 벡터 사이의 각도로 나타내며 벡터의 끝을 연결할 때 통계적으로 유의하면 점선으로 표현하여 부분가설검정의 통계적 분석결과를 인지할 수 있도록 작성한다. 이 방법을 이용하면 전진선택과 후진제거 방법에 의한 최종모형을 비교 분석하고 전체적으로 모형의 적합도를 파악할 수 있다.