• The Research Journal of the Costume Culture
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• v.28 no.2
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• pp.181-198
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• 2020

Using the developed wedding planner selection criteria scale, this study examined whether wedding planner selection criteria differ according to consumer characteristics such as demographic characteristics and wedding preparation behaviors. The main survey for this study was conducted via the Internet with 295 consumers aged 20-30 living in the Seoul metropolitan area. The data collected from the survey processed and analyzed using the statistical programs SPSS 21.0 t-test. Analyzing how wedding planner selection criteria differ according to consumers' demographic characteristics and wedding preparation behaviors, results shown for the wedding planner selection criteria were all four points on average except for individual characteristics and important sub-factors regardless of the consumers' characteristics, and various results were derived depending on the consumers' characteristics. This study has various practical implications in that it verified the difference in wedding planner selection criteria according to consumer characteristics and determined how much money consumers were willing to play for wedding planners. It is recommended that future studies take various approaches to investigate how wedding planner users are satisfied with or place importance on wedding planner services and conduct empirical using the selection criteria developed in this study to compare influential variables that affect behavior intention and willingness to pay according to consumer type.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5132-5148
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• 2017

Cyber attacks are evolving commensurate with recent developments in information security technology. Intrusion detection systems collect various types of data from computers and networks to detect security threats and analyze the attack information. The large amount of data examined make the large number of computations and low detection rates problematic. Feature selection is expected to improve the classification performance and provide faster and more cost-effective results. Despite the various feature selection studies conducted for intrusion detection systems, it is difficult to automate feature selection because it is based on the knowledge of security experts. This paper proposes a feature selection technique to overcome the performance problems of intrusion detection systems. Focusing on feature selection, the first phase of the proposed system aims at constructing a feature subset using a sequential forward floating search (SFFS) to downsize the dimension of the variables. The second phase constructs a classification model with the selected feature subset using a random forest classifier (RFC) and evaluates the classification accuracy. Experiments were conducted with the NSL-KDD dataset using SFFS-RF, and the results indicated that feature selection techniques are a necessary preprocessing step to improve the overall system performance in systems that handle large datasets. They also verified that SFFS-RF could be used for data classification. In conclusion, SFFS-RF could be the key to improving the classification model performance in machine learning.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• Journal of the Korean Society of Clothing and Textiles
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• v.7 no.1
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• pp.27-35
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• 1983

The purpose of this study is to find out the relationship between needs and factors of clothing Selection. 400 women students from 4 universities and colleges in Seoul were selected and tested. And Hwang Jeongkyu's Needs inventory test sheets for need inventory test and the questionnaire based on Lee Eunju's study for factors of clothing selection were given to the sample. The test data was computerized to get the relationship. The results are as follows: (I) Behavior of clothing selection according to expression of individuality correlated significantly with achievement, aggression, dominance, emotionality, exhibitionism, sex, and autonomy at the .01 level. (2) Behavior of clothing selection according to utility correlated significantly with abasement, and affiliation at the .01 level. and with emotionality at the .05 level, (3) Behavior of clothing selection according to economy correlated significantly with abasement, affiliation, dominance, emotionality, and exhibitionism at the .05 level, and with achievement at the .01 level. (4) Behavior of clothing selection according to modesty correlated significantly with abasement, exhibitionism, and sex at the .01 level. (5) Behavior of clothing selection according to conformity correlated significantly with abasement, emotionality, and autonomy at the .01 level, and with dominance at the .05 level.

• Journal of information and communication convergence engineering
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• v.15 no.1
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• pp.53-61
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• 2017

The preferred feature selection methods for text classification are filter-based. In a common filter-based feature selection scheme, unique scores are assigned to features; then, these features are sorted according to their scores. The last step is to add the top-N features to the feature set. In this paper, we propose an improved global feature selection scheme wherein its last step is modified to obtain a more representative feature set. The proposed method aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in the proposed method to label features according to their discriminative power on classes; these labels are used while producing the feature sets. Experimental results obtained using the well-known 20 Newsgroups and Reuters-21578 datasets with the k-nearest neighbor algorithm and a support vector machine indicate that the proposed method improves the classification performance in terms of a widely known metric ($F_1$).

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.466-471
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• 2006

IAn important problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. In order to extract feature set, Principal component analysis has been usually used and SFS(Sequential Forward Selection) and SBS(Sequential Backward Selection) have been used as a feature selection method. This paper applies genetic algorithm which is a popular method for nonlinear optimization problem to the feature selection problem. So, we call it Genetic Algorithm Feature Selection(GAFS) and this algorithm is compared to other methods in the performance aspect.

• Journal of the Korean Home Economics Association
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• v.23 no.4
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• pp.1-7
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• 1985

The aim of this study is to find out the relation-ship between personality and te factors for clothing selection. For the test, 396 high school girl students in Seoul were selected, respectively. The general personality test sheets by Kim Giseok and the questionnaire besed on Park Eunju's study on the factors for clothing selection were given to the sample group. RESULTS : 1. Behavior of clothing selection according to expression of individuality is sign ificantly correlated with ascendancy and sociability at the level of .001, and with emotional stability at .50. 2. Behavior of clothing selection according to utility is significantly correlated with resopnsibility and emotional stability at the level of .01. 3. Behavior of clothing selection according to economy is significantly correlated with ascendancy and self-confidence at the level of .01, and with responsibility and sociablity at .05. 4. Behavior of clothing selection according to modesty is significantly correlated with ascendancy and sociability at level of .001, and with self-confidence at .01. 5. Behavior of clothing selection according to conformity is significantly correlated with ascendancy, emothional stability, and self-confidence at the level of .001, and with sociability at .05.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.12
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• pp.1690-1694
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• 2002

Genetic parameters were estimated for birth weight and weaning weight from three year (1991-1993) data totalling 1100 records of 25 rams to 205 ewes of Indigenous Sabi flock maintained at Grasslands Research Station in Zimbabwe. AIREML procedures were used fitting an Animal Model. The statistical model included the fixed effects of year of lambing, sex of lamb, birth type and the random effect of ewe. Weight of ewe when first joined with ram was included as a covariate. Direct heritability estimates of 0.27 and 0.38, and maternal heritability estimates of 0.24 and 0.09, were obtained for birth weight and weaning weight, respectively. The total heritability estimates were 0.69 and 0.77 for birth weight and weaning weight, respectively. Direct-aternal genetic correlations were high and positive. The corresponding genetic covariance estimates between direct and maternal effects were positive and low, 0.25 and 0.18 for birth weight and weaning weight, respectively. Responses to selection were 0.8 kg and 0.14 kg for birth weight and weaning weight, respectively. The estimated expected correlated response to selection for birth weight by directly selecting for weaning weight was 0.26. Direct heritabilities were moderate; as a result selection for any of these traits should be successful. Maternal heritabilities were low for weaning weight and should have less effect on selection response. Indirect selection can give lower response than direct selection.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.131-139
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• 2013

Support vector machines(SVM) may perform poorly in the presence of noise variables; in addition, it is difficult to identify the importance of each variable in the resulting classifier. A feature selection can improve the interpretability and the accuracy of SVM. Most existing studies concern feature selection in the linear SVM through penalty functions yielding sparse solutions. Note that one usually adopts nonlinear kernels for the accuracy of classification in practice. Hence feature selection is still desirable for nonlinear SVMs. In this paper, we compare the performances of nonlinear feature selection methods such as component selection and smoothing operator(COSSO) and kernel iterative feature extraction(KNIFE) on simulated and real data sets.