• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1099-1107
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• 2017

In this study, we analyzed the determinants of wages of college graduates by using the data of "2014 Graduates Occupational Mobility Survey" conducted by Korea Employment Information Service. In general, wages contain two complex pieces of information about whether an individual is employed and the size of the wage. However, in many previous researches on wage determinants, sample selection bias tends to be generated by performing linear regression analysis using only information on wage size. We used the Heckman sample selection models for analysis to overcome this problem. The main results are summarized as follows. First, the validity of the Heckman's sample selection model is statistically significant. Male is significantly higher in both job probability and wage than female. As age increases and parents' income increases, both the probability of employment and the size of wages are higher. Finally, as the university satisfaction increases and the number of certifications acquired increased, both the probability of employment and the wage tends to increase.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1149-1157
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• 2013

In this study we compare academic achievements of college students admitted by admissions officer selection with those admitted by general selection. Two measurements of the academic achievements considered are GPA (grade point average) and relative ascending rank of GPA. By the comparison of the academic achievements, we would like to assess the effectiveness of the admissions office selection and then provide a basis for screening good students by that selection. The results of data analysis indicate that the academic achievements of admissions officer selection students tend to be lower than those of early general admission students and also those of regular general admission students tend to be higher than those of early general admission students.

• 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 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 ($F_1$).

• Culinary science and hospitality research
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• v.23 no.7
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• pp.31-41
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• 2017

The purpose of this study was to verify the relationship of eating-out consumption propensity and selection attributes by consumers have been used dessert cafe and to provide the useful data for efficient establishing marketing strategies to dessert cafe managers. This survey was conducted from 7th to 22th on April, 2017 and a total of 250 responses were distributed, of which 232 were used for analysis, after excluding responses containing missing data. The results from this study are as follows. First, it was found that eating out enjoyment pursuit type and health pursuit type had significant effects on menu and service factor of selection attributes for dessert cafe. Second, only eating out enjoyment pursuit type had significant effects on visual factor of selection attributes for dessert cafe. Third, only health pursuit type had significant effects on health menu factor of selection attributes for dessert cafe. Fourth, economic value pursuit type and atmosphere pursuit type had significant effects on price factor of selection attributes for dessert cafe. This study contributes to useful results for establishing efficient marketing strategies to dessert cafe marketers by examining selection attribution of dessert cafe as recognizing eating-out consumption propensity.

• Korean Journal of Acupuncture
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• v.19 no.2
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• pp.119-131
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• 2002

In this paper, we studied Dongssi' acupuncture therapy via the consideration of development process of Oriental medicine in history. We investigated the distribution chart and naming of Dongssi' acupuncture point in human body, artificial selection principle of Dongssi' acupuncture point to therapy (選穴原則) on the various diseases, the therapy of pyo-bon (標本理論) and the therapy of Geun-Gyeal (根結理論) and compared GeoZa-principle (巨刺法) and MuZa-principle (繆刺法) with artificial selection principle of Dongssi' acupuncture point. And we also studied the acupuncture therapy of DongGi (動氣鍼法), DoMa (倒馬鍼法) and SaeIn (索引鍼法), which is the unique principle in Dongssi' acupuncture theraphy, to consider with the other Oriental medicine theory which is the theory of ZangSang (臟象學說) and BiWi (脾胃學說) etc. Our desire in this study is the giving aid to treatment diseases with the acupuncture therapy in Oriental medicine.

• Genomics & Informatics
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• v.4 no.3
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• pp.110-117
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• 2006

In microarray technology, many diverse experimental features can cause biases including RNA sources, microarray production or different platforms, diverse sample processing and various experiment protocols. These systematic effects cause a substantial obstacle in the analysis of microarray data. When such data sets derived from different experimental processes were used, the analysis result was almost inconsistent and it is not reliable. Therefore, one of the most pressing challenges in the microarray field is how to combine data that comes from two different groups. As the novel trial to integrate two data sets with batch effect, we simply applied standardization to microarray data before the significant gene selection. In the gene selection step, we used new defined measure that considers the distance between a gene and an ideal gene as well as the between-slide and within-slide variations. Also we discussed the association of biological functions and different expression patterns in selected discriminative gene set. As a result, we could confirm that batch effect was minimized by standardization and the selected genes from the standardized data included various expression pattems and the significant biological functions.

• Bulletin of the Korean Mathematical Society
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• v.49 no.2
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• pp.249-262
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• 2012

In this paper, under some suitable conditions and in virtue of a selection which depends on a vector-valued function and a feasible set map, the sensitivity analysis of a class of implicit multifunctions is investigated. Moreover, by using the results established, the solution sets of parametric vector optimization problems are studied.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.10a
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• pp.131-132
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• 2005

• ETRI Journal
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• v.40 no.2
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• pp.237-245
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• 2018

The Distributed Modulated Wideband Converter (DMWC) is a networking system developed from the Modulated Wideband Converter, which converts all sampling channels into sensing nodes with number variables to implement signal undersampling. When the number of sparse subbands changes, the number of nodes can be adjusted flexibly to improve the reconstruction rate. Owing to the different attenuations of distributed nodes in different locations, it is worthwhile to find out how to select the optimal sensing node as the sampling channel. This paper proposes the spectrum sensing of DMWC based on a Markov random field (MRF) to select the ideal node, which is compared to the image edge segmentation. The attenuation of the candidate nodes is estimated based on the attenuation of the neighboring nodes that have participated in the DMWC system. Theoretical analysis and numerical simulations show that neighboring attenuation plays an important role in determining the node selection, and selecting the node using MRF can avoid serious transmission attenuation. Furthermore, DMWC can greatly improve recovery performance by using a Markov random field compared with random selection.