• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1371-1383
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• 2014

Classification ensemble technique is a method to combine diverse classifiers to enhance the accuracy of the classification. It is known that an ensemble method is successful when the classifiers that participate in the ensemble are accurate and diverse. However, it is common that an ensemble includes less accurate and similar classifiers as well as accurate and diverse ones. Ensemble pruning method is developed to construct an ensemble of classifiers by choosing accurate and diverse classifiers only. In this article, we proposed an ensemble pruning method called WAVE-bagging. We also compared the results of WAVE-bagging with that of the existing pruning method called Lasso-bagging. We showed that WAVE-bagging method performed better than Lasso-bagging by the extensive empirical comparison using 26 real dataset.

• Journal of Environmental Impact Assessment
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• v.23 no.1
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• pp.39-50
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• 2014

In recent years, the frequency and scale of the natural disasters are growing rapidly due to the global climate change. In case of the urban flooding, high-density of population and infrastructure has caused the more intensive damages. In this study, we analyzed the spatial characteristics of urban flooding damage factors using GWR(Geographically Weighted Regression) for effective disaster prevention and then, classified the causes of the flood damage by spatial characteristics. The damage factors applied consists of natural variables such as the poor drainage area, the distance from the river, elevation and slope, and anthropogenic variables such as the impervious surface area, urbanized area, and infrastructure area, which are selected by literature review. This study carried out the comparative analysis between OLS(Ordinary Least Square) and GWR model for identifying spatial non-stationarity and spatial autocorrelation, and in the results, GWR model has higher explanation power than OLS model. As a result, it appears that there are some differences between each of the flood damage areas depending on the variables. We conclude that the establishment of disaster prevention plan for urban flooding area should reflect the spatial characteristics of the damaged areas. This study provides an improved understandings of the causes of urban flood damages, which can be diverse according to their own spatial characteristics.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.155-175
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• 2014

This study targets to recognize needs of spatial pattern analysis and to draw the relationship between relocation of Capital Region firms and population outflow in Capital Region through the regression analysis. The population outflow in Capital Region has moved to and around Yesan-gun and Asan-si. Also, such outflow is found to compose mostly one or two household members for their jobs. In addition to this study has analyzed to find effect factors through the Geographically Weighted Regression. The results of the analysis has confirmed that the most decisive factors affecting population flow from Capital Region to Chungcheongnam-do were population factors and transportation factors and others. Thus, the below policy implications could be derived and also may be applied toward Sejong City which are currently experiencing the relocating of Public sectors and new constructions. Firstly, the effect of Capital Region firms movement on population inflows could be better observed in small-scale towns like "kun" than larger-scale towns like "si.". On the other hand, people in Capital Region moved to larger-scale towns like "si" unlike the Capital Region firms. This difference implicates that people select their residence according to not only their jobs but also residential environment. Secondly, moving people from Capital Region to another region for their jobs are expected to appear more in a form of family units rather than individual units. Sejong city, where public organizations are being relocated, should recognize this particular Chungcheonnam-do phenomenon and be prepared to be more effectively used in perspectives of land use as well as urban planning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.47-55
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• 2019

The army of republic of korea have been continued to transform their logistics support system structure for better efficient logistics support system in preparation for the future environment. Logistics system has supply network structure which is connected by various units and supply network structure received attention as a factor of success of supply network. Many researchers have continuously researched inventory management, transportation or economy factors for supply network, but such a study on the one in military supply network structure analysis is still slower than the study of analysis of other factors until now. In this study, we identify military supply network structure influence factor by application of social network analysis method which is used broadly and analyze co-relationships between supply network structure influence factor and valued APL(average path length) as a criteria of efficiency of military supply network. By this study it has value of military supply network influence factor identification for the better military supply network fabrication.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.4
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• pp.55-64
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• 2014

While small and medium-sized companies are suffering from a shortage of workers as a result of social tendency to avoid those companies, college graduates still prefer large companies or governmental positions, which consequently results in inconsistencies in the demand and supply of work forces. The gap between them is getting so bad that employment difficulties are exacerbating. Accordingly this study tries to search for potential employee's expected value factors which make people select small and medium companies not big companies. A survey was conducted from October 1 to october 30, 2012 with university students in the Seoul metropolitan area. a total of 350 questionnaires were distributed and 335 were collected. of these, 332 questionnaires were used for data analyses excluding questionnaires with missing values. Data was analyzed by frequency, descriptive factor, reliability, and regression with SPSS win 18.0 program The result of this study were as follows. A factor analysis extracted four factors comprising small and medium companies, which we named career(factor 1), working environment(factor 2), working achievement(factor 3), job security (factor 4). This study showed that small and medium companies' preference were affected by the career, working environment, job security, corporate reputation, salary.

• Journal of the Korean association of regional geographers
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• v.14 no.6
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• pp.736-751
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• 2008

Although a growing number of studies have commonly used a simple areal weighting interpolation method to quantify demographic characteristics of impacted areas in environmental equity analysis, the results obtained are inevitably imprecise because of the method's unrealistic assumption that population is evenly distributed within a census enumeration unit. Two alternative areal interpolation methods such as intelligent areal weighting and regression methods can account for the distributional biases in the estimation of impacted populations by making use of additional information about the geographic distribution of population. This research explores five areal interpolation methods for estimating the population characteristics of impacted areas in environmental equity analysis and evaluates the sensitivity of the outcomes of environmental equity analysis to areal interpolation methods. This study used GIS techniques to allow areal interpolation to be informed by the distribution of land cover types, as inferred from a satellite image. in both the source and target units. Independent samples t-test statistics were measured to verify the environmental equity hypothesis while coefficients of variation were calculated to compare the relative variability and consistency in the socioeconomic characteristics of populations at risk over different areal interpolation methods. Results show that the outcomes of environmental equity analysis in the study area are not sensitive to the areal interpolation methods used in estimating affected populations, but the population estimates within the impacted areas are largely variable as different areal interpolation methods are used. This implies that the use of different areal interpolation methods may to some degree alter the statistical results of environmental equity analysis.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.73-81
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• 2013

Ordinary least squares (OLS) regression is the primary statistical method in previous studies for vegetation distribution patterns in relation to landform. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between NDVI (Normalized Difference Vegetation Index) patterns and changing trends of landform (elevation, slope) and solar intensity (insolation and duration of sunshine) in Mt Geum-gang of North-Korea. Results denoted that GWR was more powerful than OLS in interpreting relationships between NDVI patterns and landform/solar intensity, since GWR was characterized by higher adjusted R2, and reduced spatial autocorrelations in model residuals. Unlike OLS regression, GWR allowed the coefficients of explanatory variables to differ by locality by giving relatively more weight to NDVI patterns which are affected by local landform and solar factors. The strength of the regression relationships in the GWR increased significantly, by showing regression coefficient of higher than 70% (0.744) in the southern ridge of the experimental area. It is anticipated that this research output will serve to increase the scientific and objective vegetation monitoring in relation to landform and solar intensity by overcoming serious constraints suffered from the past non-GWR-based approach.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.989-998
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• 2018

Connectivity between water source and demand node can be served as a critical system performance indicator of the degree of water distribution network (WDN)' failure severity under abnormal conditions. Graph theory-based approaches have been widely applied to quantify the connectivity due to WDN's graph-like topological feature. However, most previous studies used undirected-unweighted graph theory which is not proper to WDN. In this study, the directed-weighted graph theory was applied for WDN connectivity analyses. We also proposed novel connectivity indicators, Source-to-Node Shortest Pathway (SNSP) and SNSP-Degree (SNSP-D) which is an inverse of the SNSP value, that does not require complicate hydraulic simulation of a WDN of interest. The proposed SNSP-D index was demonstrated in total 42 networks in J City, South Korea in which Pearson Correlation Coefficient (PCC) between the proposed SNSP-D and four other system performance indicators was computed: three resilience indexes and an energy efficiency metric. It was confirmed that a system representative value of the SNSP-D has strong correlation with all resilience and energy efficiency indexes (PCC = 0.87 on average). Especially, PCC was higher than 0.93 with modified resilience index (MRI) and energy efficiency indicator. In addition, a multiple linear regression analysis was performed to identify the system hydraulic characteristic factors that affect the correlation between SNSP-D and other system performance indicators. The proposed SNSP is expected to be served as a useful surrogate measure of resilience and/or energy efficiency indexes in practice.

• The Korean Journal of Financial Management
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• v.21 no.1
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• pp.87-124
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• 2004

Some studies indicate that investors systematically underreact to new information in the stock market and Other studies indicate that investors systematically overreact. If investors irrationally react to the R&D intensity information, The portfolio strategy based on the R&D intensity information will be provided substantial excess returns. This study investigate that investors systematically underreact or overreact to the R&D intensity and whether portfolio strategy based on the R&D intensity is useful or not. Major results we as follows. First, This study indicate that investor systematically underreact to high R&D intensity and overreact low R&D intensity information. Second, after controlling the firm's specific factor such as firm size, BV/MV and past price performance, it is found that the performance of portfolio strategy based on the R&D intensity is not significant.

• Analytical Science and Technology
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• v.29 no.1
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• pp.19-28
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• 2016

The objective of this study was to determine the acidification of precipitation in the Jeju area. Precipitation samples were collected from the Jeju area from 2009-2014, and the major ionic species were analyzed. In the regression analysis, through a comparison of ion balance, electric conductivity, and acid fraction, the correlation coefficients showed a good linear relationship within the range of 0.927~0.983. The volume-weighted means of the pH and electric conductivity were 4.9 and 22.7 µS/cm, respectively. The ionic strength of precipitation was 0.27±0.38 mM, indicating about 35.9 % of total precipitation within the pure precipitation criteria. The volume-weighted mean concentrations (ìeq/L) of the ionic species in the precipitation were in the order of Na⁺ > Cl⁻ > nss-SO₄²⁻ > NO₃⁻ > NH₄⁺ > Mg²⁺ > H⁺ > nss-Ca²⁺ > PO₄³⁻ > K⁺ > HCOO⁻ > CH₃COO⁻ > NO₂⁻ > F⁻ > HCO₃⁻ > CH₃SO₃⁻ . The acidification contributions by sulfuric and nitric acids were 54.5 % and 36.5 %, respectively. Meanwhile the acidification contributions by formic and acetic acids were 4.8 % and 4.2 %, respectively. Thus, it was found that the acidification of the precipitation in the Jeju area was mainly due to the inorganic acids. The neutralization factors by NH₃ and CaCO₃ were also 33 % and 20 %, respectively.