• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.179-186
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• 2008

In this paper we analyse the effects of Bayesian algorithm in teaming class imbalance problems and compare the performance evaluation methods. The teaming performance of the Bayesian algorithm is evaluated over the class imbalance problems generated by priori data distribution, imbalance data rate and discrimination complexity. The experimental results are calculated by the AUC(Area Under the Curve) values of both ROC(Receiver Operator Characteristic) and PR(Precision-Recall) evaluation measures and compared according to imbalance data rate and discrimination complexity. In comparison and analysis, the Bayesian algorithm suffers from the imbalance rate, as the same result in the reported researches, and the data overlapping caused by discrimination complexity is the another factor that hampers the learning performance. As the discrimination complexity and class imbalance rate of the problems increase, the learning performance of the AUC of a PR measure is much more variant than that of the AUC of a ROC measure. But the performances of both measures are similar with the low discrimination complexity and class imbalance rate of the problems. The experimental results show 4hat the AUC of a PR measure is more proper in evaluating the learning of class imbalance problem and furthermore gets the benefit in designing the optimal learning model considering a misclassification cost.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.115-127
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• 2023

Multinomial probit model is a popular model for multiclass classification and choice model. Markov chain Monte Carlo (MCMC) method is widely used for estimating multinomial probit model, but its computational cost is high. However, it is well known that variational Bayesian approximation is more computationally efficient than MCMC, because it uses subsets of samples. In this study, we describe multinomial probit model with Gaussian process classification and how to employ variational Bayesian approximation on the model. This study also compares the results of variational Bayesian multinomial probit model to the results of naive Bayes, K-nearest neighbors and support vector machine for the UCI mice protein expression level data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.135-146
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• 2019

In this paper, the preventive maintenance strategy in operation stage of a bridge using Bayesian inference is proposed. The proposed technique can be used to predict the variation in the performance (or condition) of the bridge with higher accuracy, considering the uncertainty of monitoring. The applicability of the proposed method to the existing bridges is verified and analyzed that have an advantage in terms of maintenance cost efficiency compared to the conventional periodic maintenance system, which establishes maintenance after damage. It is expected that the proposed preventive maintenance method can be used to overcome the limitation of the conventional periodic maintenance method and to make practical bridge maintenance decision.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1092-1100
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• 2004

An E-mails have regarded as one of the most popular methods for exchanging information because of easy usage and low cost. Meanwhile, exponentially growing unwanted mails in user's mailbox have been raised as main problem. Recognizing this issue, Korean government established a law in order to prevent e-mail abuse. In this paper we suggest hybrid spam mail filtering system using weighted Bayesian classifier which is extended from naive Bayesian classifier by adding the concept of preprocessing and intelligent agents. This system can classify spam mails automatically by using training data without manual definition of message rules. Particularly, we improved filtering efficiency by imposing weight on some character by feature extraction from spam mails. Finally, we show efficiency comparison among four cases - naive Bayesian, weighting on e-mail header, weighting on HTML tags, weighting on hyperlinks and combining all of four cases. As compared with naive Bayesian classifier, the proposed system obtained 5.7% decreased precision, while the recall and F-measure of this system increased by 33.3% and 31.2%, respectively.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.57-59
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• 2001

The restructuring of power industry is still going on all over the world for last several decades. Many kinds of restructuring model has been studied, proposed, and applied. Among those models, power pool is more popular than others. This paper assumes the power pool market structure having competitive generation sector and a new method is presented to build bidding strategy in that market. The utilities participating in the market have the perfect information on their cost and price functions, but they don't know the strategy to be chosen by others. To define one's strategy as a vector, we make utility's cost/price function into discrete step function. An utility knows only his own strategy, so he estimates the other's strategy using stochastic methods. For considering these conditions, we introduce the Bayesian rules and noncooperative game theory concepts. Also additional assumptions are included for simplification of solving process. Each utility builds the strategy to maximize his own expected profit function using noncooperative Bayesian game. A numerical example is given in case study to show essential features of this approach.

• Journal of Navigation and Port Research
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• v.41 no.5
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• pp.259-268
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• 2017

Formal Safety Assessment (FSA) has been mostly implemented on the hardware aspects of vessels. Although there are guidelines regarding human error FSAs, there have not been many assessments in such areas. To this end, this study seeks to use precedent studies for the safe operation of DP vessels, conducting an FSA regarding human error of DP LOP (Loss of Position) incidents. For this, the study referred to precedent studies for the frequency of DP LOP incidents caused by human errors, adding the severity of LOP incidents, and then applying them to the Bayesian network. As a result, the study was able to confirm that among DP LOP incidents caused by human errors, the drive-off from skill-based errors was 74.3% and the drive-off from unsafe supervision was 50.5%. Based on such results, RCOs (Risk Control Options) were devised through a brainstorming session with experts coming up with proposals including providing mandatory DPO training, installing DP simulator on the vessels, drawing up measures to understanding the procedures for safe operation of DP vessels. Moreover, it was found that mandatory DPO training is reasonable in terms of cost benefits and that while installing a DP simulator is not suitable in terms of cost benefits, it can significantly reduce risks when operating DP vessels.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.709-720
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• 2017

In healthcare and medical research, many important variables have a measurement error such as body mass index and laboratory data. It is also not easy to collect samples of large size because of high cost and long time required to collect the target patient satisfied with inclusion and exclusion criteria. Beside, the demand for solving a complex scientific problem has highly increased so that a semiparametric regression approach could be of substantial value solving this problem. To address the issues of measurement error, small domain and a scientific complexity, we conduct a multivariable Bayesian smoothing under structural measurement error covariate in this article. Specifically we enhance our previous model by incorporating other useful auxiliary covariates free of measurement error. For the regression spline, we use a radial basis functions with fixed knots for the measurement error covariate. We organize a fully Bayesian approach to fit the model and estimate parameters using Markov chain Monte Carlo. Simulation results represent that the method performs well. We illustrate the results using a national survey data for application.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1191-1204
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• 2017

Weighted least squares (WLS) estimation is often easily used for the data with heteroscedastic errors because it is intuitive and computationally inexpensive. However, WLS estimator is less robust to a few outliers and sometimes it may be inefficient. In order to overcome robustness problems, Box-Cox transformation, Huber's M estimation, bisquare estimation, and Yohai's MM estimation have been proposed. Also, more efficient estimations than WLS have been suggested such as Bayesian methods (Cepeda and Achcar, 2009) and semiparametric methods (Kim and Ma, 2012) in heteroscedastic error models. Recently, Çelik (2015) proposed the weight methods applicable to the heteroscedasticity patterns including butterfly-distributed residuals and megaphone-shaped residuals. In this paper, we review heteroscedastic regression estimators related to robust or efficient estimation and describe their properties. Also, we analyze cost data of U.S. Electricity Producers in 1955 using the methods discussed in the paper.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.193-196
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• 1997

In this paper we consider a Bayesian theoretic approach to periodic incomplete preventive maintenance with minimal repair at failure. We assume that the system failure rate is increasing as the frequency of PM increases and that the system is replaced at the K-th PM under this maintenance strategy. The optimal policies which minimize the expected cost rates are discussed. We seek the optimal periodic PM interval x and replacement time K under a Weibull failure intensity. Assuming suitable prior distribution for the Weibull parameters, we derive the posterior distribution incorporating failure data and obtain the updated optimal replacement strategies.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.952-960
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• 2009

Foundation soils are greatly influenced on the stability of structures. The soft clay deposited in Incheon Port area is named either nomally consolidated clay or unconsolidated clay. New harbor structures will be constructed in Incheon Port area. Not sufficient soil boring datum are obtained from the filed soil exploration due to limited time and cost. The harbor construct site is pretty large area and the soils are not homogenous. This paper presented the result of reliability analysis which was performed by both Bayesian approach and analysis of variance.