• Journal of Science Education
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• v.39 no.2
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• pp.278-289
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• 2015

The purpose of this study is to analyze selection methods of gifted students. This study focuses on the understanding the characteristics of gifted students in accordance with the selection methods, i.e. the examination selection and automatic promotion by analyzing and comparing the academic emotion, meta-cognition, and self-efficacy between gifted students selected according to the selection methods. Moreover, for the purpose of the effective gifted education, this study aims to arrange a reasonable basis for the discrimination and selection of gifted students. The results of this study were as follows. First, there was no meaningful difference between gifted students selected by an examination and promoted automatically in view of academic emotion, meta-cognition, and self-efficacy of gifted students. It is determined that there is no difference between the effects of selection methods under the condition of the same group of gifted students. Second, regarding the academic emotion of gifted students, there is no significant difference in both the elementary and middle school in case of examination selection. However, in case of the automatic promotion, the academic emotion of gifted students promoted automatically in the gifted education center was higher than that of the gifted students in the gifted class (p < .05). Regarding the meta-cognitive skill, there is no difference in the elementary school between the selection methods. In case of the examination selection in the middle school, the meta-cognitive skill of male students of the gifted education center was higher than that of the female and gifted class students (p < .05). In case of the automatic promotion in the middle school, the meta-cognitive skill of students of the gifted education center was higher than that of students of the gifted class (p < .05). As for the case of self-efficacy, there were no differences between the selection methods. In the automatic promotion, self-efficacy of students of the gifted education center was higher than that of students of the gifted class (p < .05).

• Communications for Statistical Applications and Methods
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• v.29 no.6
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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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• v.5 no.3
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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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• v.22 no.2
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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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• v.25 no.1
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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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• v.20 no.3
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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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• v.20 no.3
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• pp.9-46
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• 2013

The degree of delegation related to the social security acts is involved in the candidate selection methods and the standing committee system. The social security acts with a small amount of delegation have an affinity with the bottom-up selection methods and the standing committee to guarantee long term in office. In Germany, the bottom-up selection method which guarantees the right of party members to nominate candidates and the standing committee to guarantee long term in office have an affinity with the Social Acts with less delegation. But the social security acts with a large number of delegation have an affinity with the top-down selection methods and the standing committee not to guarantee long term in office. In Korea, the top-down selection method in which the central headquarter of the party dominates the selection process, and the standing committee whose members are to be selected every two years have an affinity with the Social Security Acts with the excessive delegation.

• Journal of the Ergonomics Society of Korea
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• v.24 no.3
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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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• v.18 no.3
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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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• v.21 no.5
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• pp.901-908
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• 2010

A geometrical description method is proposed to represent the process of the forward selection and backward elimination methods among many variable selection methods for multiple regression models. This graphical method shows the process of the forward selection and backward elimination on the first and second quadrants, respectively, of half circle with a unit radius. At each step, the SSR is represented by the norm of vector and the extra SSR or partial determinant coefficient is represented by the angle between two vectors. Some lines are dotted when the partial F test results are statistically significant, so that statistical analysis could be explored. This geometrical description can be obtained the final regression models based on the forward selection and backward elimination methods. And the goodness-of-fit for the model could be explored.