• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.89-94
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• 2001

In this study we focus on variable selection in decision tree growing structure. Some of the splitting rules and variable selection algorithms are discussed. We propose a competitive variable selection method based on Kruskal-Wallis test, which is a nonparametric version of ANOVA F-test. Through a Monte Carlo study we note that CART has serious bias in variable selection towards categorical variables having many values, and also QUEST using F-test is not so powerful to select informative variables under heavy tailed distributions.

• Journal of the Korea Institute of Building Construction
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• v.8 no.5
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• pp.127-134
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• 2008

The purpose of this study is to propose the decision making technique in roof waterproofing method at the early construction stage. Selecting the suitable construction method is difficult because of the complex interrelationships between many factors of influencing the construction method selection. This study presents an example of selecting suitable method by analyzing LCC (Life Cycle Cost) in roof waterproofing work. In this study, roof waterproofing method is analyzed by LCC(Life Cycle Cost) which is consists of the initial costs, running costs, and removal costs. Sheet waterproofing, membrane waterproofing and asphalt waterproofing costs are compared to select the most economic method. The result of this study revealed that considering LCC is useful in selecting the proper method in the construction work.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.108-113
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• 2012

Routing paths are mightily important for the network security in WSNs. To maintain such routing paths, sustained path re-selection and path management are needed. Region segmentation based path selection method (RSPSM) provides a path selection method that a sensor network is divided into several subregions, so that the regional path selection and path management are available. Therefore, RSPSM can reduce energy consumption when the path re-selection process is executed. However, it is hard to guarantee optimized secure routing path at all times since the information using the path re-selection process is limited in scope. In this paper, we propose partial path selection method in each subregion using preselected partial paths made by RSPSM for routing path optimization in SEF based sensor networks. In the proposed method, the base station collects the information of the all partial paths from every subregion and then, evaluates all the candidates that can be the optimized routing path for each node using a evaluation function. After the evaluation process is done, the result is sent to each super DN using the global routing path information (GPI) message. Thus, each super DN provides the optimized secure routing paths using the GPI. We show the effectiveness of the proposed method via the simulation results. We expect that our method can be useful for the improvement of RSPSM.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.647-652
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive Feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.2
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• pp.19-25
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• 2008

When there are numerous wireless communication systems co-existing in the limited available frequency resource, an unexpected time delay can be caused during the system switching. So, in order to reduce this time delay, a mode selection method is required. In this paper, we propose a mode selection method to minimize the time delay for multi-system wireless communication systems. For the sake of efficiency, the mode selection method is designed by analyzing the preamble characteristics of different standards. Instead of performing a full search, we propose the preamble partial search to reduce the time delay to a minimum. Simulated with Matlab in an additive white Gaussian noise(AWGN) environment with a signal to noise ratio(SNR) of 10dB and bit error rate(BER) of $10^{-6}$, we evaluated and showed the performance improvement gained by using our proposed mode selection method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5631-5652
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• 2019

The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.75-84
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• 2014

In this paper, we dealt with feature selection problem of large-scale and high-dimensional biological data such as omics data. For this problem, most of the previous approaches used simple score function to reduce the number of original variables and selected features from the small number of remained variables. In the case of methods that do not rely on filtering techniques, they do not consider the interactions between the variables, or generate approximate solutions to the simplified problem. Unlike them, by combining set covering and clustering techniques, we developed a new method that could deal with total number of variables and consider the combinatorial effects of variables for selecting good features. To demonstrate the efficacy and effectiveness of the method, we downloaded gene expression datasets from TCGA (The Cancer Genome Atlas) and compared our method with other algorithms including WEKA embeded feature selection algorithms. In the experimental results, we showed that our method could select high quality features for constructing more accurate classifiers than other feature selection algorithms.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.411-422
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• 2014

This study analyzed how average GPA (grade point average) changes as the number of completed semesters increases based on the estimates of intercept, slope, and quadratic term. The students included in this study are those who was admitted in 2011 and took 6 consecutive semesters. More precisely, it was analyzed if intercept, slope and quadratic term of average GPA were different between gender and selection method. The results showed that the intercept was different between selection method, the slope was different between gender, but the quadratic term was different between neither selection method nor gender.