• Journal of Digital Contents Society
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• v.19 no.4
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• pp.739-747
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• 2018

With a large feature space data, feature selection has become an extremely important procedure in the Data Mining process. But the traditional feature selection methods with single process may no longer fit for this procedure. In this paper, we proposed a hybrid efficient feature selection model for high dimensional data. We have applied our model on KNHNAES data set, the result shows that our model outperforms many existing methods in terms of accuracy over than at least 5%.

• Journal of the Korean Society for Marine Environment & Energy
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• v.5 no.1
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• pp.19-29
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• 2002

In this paper, the selection of transport route for import-export container cargo based on the sacrifice model and CO₂ emission was investigated. At first, the transportation of import-export container cargo, the transport share of each transport route, the CO₂ gas emission, the sacrifice model and the time value of import-export container cargo were investigated. And next, the selection of transport route based on the sacrifice model was investigated for the transport of import-export container cargo from Seoul to Pusan Port. Finally, the transport route was also selected by using the sacrifice model including the effect of CO₂ emission. The research results show that the transport route selection results of import-export container cargo based on the sacrifice model represents the present status of the transportation of import-export container cargo very well. And also the research results show that the reduction of transport time was very effective to increase the share of coastal transportation.

• Journal of applied mathematics & informatics
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• v.6 no.2
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• pp.599-609
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• 1999

Neural networks are explored as an alternative to a regres-sion model for prediction of the number of daily household vehicular trips. This study focuses on contrasting a neural network model with a regression model in term of variable selection as well as the appli-cation of these models for prediction of extreme observations, The differences in the models regarding data transformation variable selec-tion and multicollinearity are considered. The results indicate that the neural network model is a viable alternative to the regression model for addressing both messy data problems and limitation in variable structure specification.

• Journal of Korean Society for Quality Management
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• v.29 no.1
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• pp.11-23
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• 2001

This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

• Journal of the Korea Safety Management & Science
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• v.1 no.1
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• pp.91-100
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• 1999

We propose a decision model to incorporates the values assigned by a group of experts on different factors in selecting robots. Using this model, SN ratio of taguchi method for each of subjective factors as well as values of weights are used in this comprehensive method for robot selection. A numerical example is presented to illustrate the model and to show a rank reversal when compared to a model that does not eliminate extreme values and eliminates the highest and lowest experts' values allocating the weights and the subjective factors.

• IE interfaces
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• v.20 no.3
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• pp.376-386
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• 2007

The analytic hierarchy process (AHP), a well-known and useful decision making method, has been applied to R&D project evaluation and selection. The objective of this study is to propose a new model for evaluating and selecting R&D projects of Samsung Electro-Mechanics, the top manufacturer of electronic components in Korea, using the AHP. To show the validity of the new model, we strived to successively compare the final priorities for R&D projects with the priorities obtained by the existing model and the new model respectively.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1059-1066
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• 2016

In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.852-861
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• 2000

Software reliability growth models are used in testing stages of software development to model the error content and time intervals betwewn software failures. In this paper, using priors for the number of fault with the negative binomial distribution nd the error rate with gamma distribution, Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability. For model selection, we explored the sum of the relative error, Braun statistic and median variation. In Bayesian computation process, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carolo method to compute the posterior distribution. Using simulated data, Bayesian inference and model selection is studied.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.92-93
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• 2019

Estimates based on BIM makes it possible to perform from quantity take-off to construction cost estimates by using model, which is made in the phase of design and construction. As the BIM models are made up of the units of element, there an advantage of the automative quantity take-off, if the correction or change of element occurs. Work items, not included in the elements of the BIM model, are excepted from bill of quantity. Level of detail(LoD) of the BIM model can be improved for detailed estimates, but an excessive modeling for estimates is inefficient. This study presents the measure for selection and quantity take-off of work items, those are not expressed in the BIM model. The proposed method avoids the creation of excessive BIM Models and enables quantity take-off in conjunction with the element.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.