• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.525-535
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• 2018

If the frequency of a particular class is excessively higher than the frequency of other classes in the classification problem, data imbalance problems occur, which make machine learning distorted. Corporate bankruptcy prediction often suffers from data imbalance problems since the ratio of insolvent companies is generally very low, whereas the ratio of solvent companies is very high. To mitigate these problems, it is required to apply a proper sampling technique. Until now, oversampling techniques which adjust the class distribution of a data set by sampling minor class with replacement have popularly been used. However, they are a risk of overfitting. Under this background, this study proposes ROSE(Random Over Sampling Examples) technique which is proposed by Menardi and Torelli in 2014 for the effective corporate bankruptcy prediction. The ROSE technique creates new learning samples by synthesizing the samples for learning, so it leads to better prediction accuracy of the classifiers while avoiding the risk of overfitting. Specifically, our study proposes to combine the ROSE method with SVM(support vector machine), which is known as the best binary classifier. We applied the proposed method to a real-world bankruptcy prediction case of a Korean major bank, and compared its performance with other sampling techniques. Experimental results showed that ROSE contributed to the improvement of the prediction accuracy of SVM in bankruptcy prediction compared to other techniques, with statistical significance. These results shed a light on the fact that ROSE can be a good alternative for resolving data imbalance problems of the prediction problems in social science area other than bankruptcy prediction.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.3
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• pp.81-84
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• 2015

Cloud service is one of major technologies in modern IT business. Due to the dynamics of user demands, service providers need VM(Virtual Machine) provisioning mechanism to predict the amount of resources demanded by cloud users for the next service and to prepare the resources. VM provisioning provides the QoS to cloud user and maximize the revenue of a service provider by minimizing the expense. In this paper, we propose a new VM provisioning technique to minimize the total expense of a service provider by minimizing the expected value of the expense based on the predicted demands of users. To evaluate the effectiveness of our prediction technique, we compare the total expense of our technique with these of the other prediction techniques with a series of real trace data.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.527-534
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• 2013

The software is changed according to the changing businesses and the user requirement, it involves increasing complexity and cost. Considering the repetitive changes required for the software, replacement is more efficient than maintenance at some point. In this study, the replacement time was predicted using the service dissatisfaction index and replacement point assessment index by the software group for each task. First, fuzzy inference was used to develop the method and indicator for the user's service level dissatisfaction. Second, the replacement point assessment method was established considering the quality, costs, and new technology of the software. Third, a replacement time prediction technique that used the gap between the user service measurement and replacement point assessment values was proposed. The results of the case study with the business solutions of three organizations, which was conducted to verify the validity of the proposed prediction technique in this study, showed that the service dissatisfaction index decreased by approximately 16% and the replacement point assessment index increased by approximately 9%.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.5
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• pp.119-127
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• 2010

This paper provides a new approach for durability prediction of reinforced concrete structures exposed to carbonation. In this method, the prediction can be updated successively by a Bayes' theorem when additional data are available. The stochastic properties of model parameters are explicitly taken into account in the model. To simplify the procedure of the model, the probability of the durability limit is determined based on the samples obtained from the Latin Hypercube Sampling(LHS) technique. The new method may be very useful in design of important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored. For using the new method, in which the prior distribution is developed to represent the uncertainties of the carbonation velocity using data of concrete structures(3700 specimens) in Korea and the likelihood function is used to monitor in-situ data. The posterior distribution is obtained by combining a prior distribution and a likelihood function. Efficiency of the LHS technique for simulation was confirmed through a comparison between the LHS and the Monte Calro Simulation(MCS) technique.

• Geotechnical Engineering
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• v.12 no.5
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• pp.103-116
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• 1996

Man-made vibrations from traffic and construction activities are important because they may cause damage to structures. The current literature provides that damages in the urban areas were not caused by direct transmission of vibration, but rather through subsequent settlement caused by soil densification. In this paper. prediction technique of ground borne vibration induced settlement was introduced on the basis of case studies. In situ application technique of the settlement prediction model developed in laboratary was described, and the predicted settlement was compared with the measured settlement from case studies. The settlement from case studies hlatched well with the settlement calculated from the model. The parametric studies of settlement in typical urban site conditions were performed to determine the sensitive parameters and to develop reliable vibration monitoring and interpretation schemes. These demonstrated the potential usefulness of the model for the evaluation and prediction of the vibration induced in-situ settlement of sands.

• Atmosphere
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• v.25 no.1
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• pp.179-183
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• 2015

Even though state-of-the-art general circulation models is improved step by step, the seasonal predictability of the East Asian summer monsoon still remains poor. In contrast, the seasonal predictability of western North Pacific and Indian monsoon region using dynamic models is relatively high. This study builds canonical correlation analysis model for seasonal prediction using wind fields over western North Pacific and Indian Ocean from the Global Seasonal Forecasting System version 5 (GloSea5), and then assesses the predictability of so-called hybrid model. In addition, we suggest improvement method for forecast skill by introducing the lagged ensemble technique.

• International Journal of Fluid Machinery and Systems
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• v.2 no.1
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• pp.21-30
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• 2009

Slurry wear with sand particles in rivers is a serious problem for pump operation. Therefore, a technique to predict wear volume loss is required for selecting wear resistant materials and determining specifications for the maintenance period. This paper reports a method for predicting the wear depth distribution on the blade of an impeller. Slurry wear tests of an aluminum pump impeller were conducted. Prediction results of wear depth distribution approximately correspond with the results of slurry wear tests. This technique is useful for industrial application.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.949-950
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• 2008

The intra prediction technique is the one of the key factors to the success of H.264. There are nine optional prediction modes for each $4{\times}4$ luma block and 4 modes for each $16{\times}16$ luma block. To reduce the intra mode bits efficiently, the most probable mode (MPM) is estimated by using the intra modes of the adjacent blocks, since intra modes for neighboring $4{\times}4$ luma blocks are correlated. In this paper, a new method for estimating the MPM is proposed by using the statistical distribution of intra modes of adjacent intra blocks. Experimental results show that the proposed method can achieve a coding gain of about 0.1dB.

• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.3
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• pp.46-54
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So various of techniques are developed to predict the construction cost accurately and expeditely. Among the techniques, Support Vector Machine(SVM) has an excellent ability for generalization performance. Therefore, the purpose of this study is to construct the prediction model for construction cost of educational building project using support vector machine technique. And to verify the accuracy of prediction model for construction cost. The performance data used in this study are 217 school building project cost which have been completed from 2004 to 2007 in Gyeonggi-Do, Korea. The result shows that average error rate was 7.48% for SVM prediction model. So using SVM model on predicting construction cost of educational building project will be a considerably effective way at the early project stage.

• Architectural research
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• v.12 no.2
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• pp.15-22
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• 2010

Our prediction model is based on the development of "Semantic Location Model." It embodies geometrical and topological information which can increase the efficiency in prediction and make it easy to manipulate the prediction model. Data mining is being implemented to extract the inhabitant's location patterns generated day by day. As a result, the self-learning system will be able to semantically predict the inhabitant's location in advance. This context-aware system brings about the key component of the ubiquitous computing environment. First, we explain the semantic location model and data mining methods. Then the location prediction model for the ubiquitous computing system is described in details. Finally, the prototype system is introduced to demonstrate and evaluate our prediction model.