• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1045-1054
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• 2012

Support vector machine is known to be the very useful statistical method in classification and nonlinear function estimation. In this paper we propose a monotone support vector regression (SVR) for the estimation of monotonically decreasing function. The proposed monotone SVR is applied to smooth the Kaplan-Meier estimate of survival function. Experimental results are then presented which indicate the performance of the proposed monotone SVR using survival functions obtained by exponential distribution.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.629-637
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• 2022

It is at risk of depletion of biodiversity due to indiscriminate overfishing of ecosystems and destruction of habitats. Intensive fertilizers or development of related facilities to increase agricultural production in poor indigenous areas devastate the soil. Preservation of biodiversity is now emerging as an important issue of global human coexistence. After the Post-2020 GBF Declaration, all governance in agricultural development in indigenous agricultural areas should be supported and promoted as biodiversity conservation measures. A compromise plan to reduce ecosystem development and biodiversity loss can help establish public governance policies. In this paper, a viability kernel used for viable control feedback analysis is introduced to solve conflicting economic and ecological problems in ecosystem conservation, and a mathematical model on biodiversity conservation by the viability kernel is examined. Because all species in the ecosystem are interdependent, if the balance is broken, biodiversity is depleted, which is irreversible and eventually leads to extinction. For sustainable use and harmony of biological resources, a lot of policy consideration is required, such as creative governance that can efficiently protect all species. Subsidies or tax incentives have a direct impact on biodiversity conservation. The recovery of species in a state of decreasing biodiversity can be said to be of great economic value. Biodiversity will allow indigenous producers to be proud of their unique traditional knowledge and have a positive impact on local tourism, thereby enhancing regional identity and greatly contributing to the survival and prosperity of mankind.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.739-748
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• 2012

In this paper we propose a mixed effects least squares support vector machine (LS-SVM) for the censored data which are observed from different groups. We use weights by which the randomly right censoring is taken into account in the nonlinear regression. The weights are formed with Kaplan-Meier estimates of censoring distribution. In the proposed model a random effects term representing inter-group variation is included. Furthermore generalized cross validation function is proposed for the selection of the optimal values of hyper-parameters. Experimental results are then presented which indicate the performance of the proposed LS-SVM by comparing with a standard LS-SVM for the censored data.

• Journal of the Economic Geographical Society of Korea
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• v.23 no.4
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• pp.408-424
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• 2020

This study attempts to analyze the spatial clustering of survived and closed coffee shops based on the land price and land use for each coffee shop location. The locational characteristics of survived and closed coffee shops for each cluster type are identified through various locational properties such as transport factors (physical accessibility), shop properties (franchise information, newly open/closed business experience), and spatial density (kernel density estimation). To this end, we categorize the clusters of survived and closed coffee shops into three types (general locational distribution type, commercialization type of residential area and location type of commercial center), and then analyze their locational characteristics. As the result, we found that the locations of newly open and closed coffee shops show different distribution characteristics, even though they are classified into the same type due to the double sidedness of new open and closed locations. The results of this study can be provided as basic data for planning the location of coffee shop as well as regional commercial district.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.