• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.355-362
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• 2016

Ensemble methods often help increase prediction ability in various predictive models by combining multiple weak learners and reducing the variability of the final predictive model. In this work, we demonstrate that ensemble methods also enhance the accuracy of prediction under kernel ridge regression and kernel logistic regression classification. Here we apply bagging and random forests to two kernel-based predictive models; and present the procedure of how bagging and random forests can be embedded in kernel-based predictive models. Our proposals are tested under numerous synthetic and real datasets; subsequently, they are compared with plain kernel-based predictive models and their subsampling approach. Numerical studies demonstrate that ensemble approach outperforms plain kernel-based predictive models.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.655-660
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• 2003

In general, Support Vector Machine has a good performance in binary classification, but it has the limitation on multi-pattern classification. So, we proposed an Import Vector Voting model for two or more labels classification. This model applied kernel bagging strategy to Import Vector Machine by Zhu. The proposed model used a voting strategy which averaged optimal kernel function from many kernel functions. In experiments, not only binary but multi-pattern classification problems, our proposed Import Vector Voting model showed good performance for given machine learning data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.275-278
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• 2002

Vapnik이 제안한 Support Vector Machine은 두 개의 부류를 갖는 데이터에 대한 분류에는 매우 좋은 성능을 보인다는 점은 이미 잘 알려져 있다. 하지만 부류의 개수가 3개 이상인 다중 패턴을 갖는 데이터에 대한 분류에는 SVM을 적용하기가 쉽지 않다. Support Vector Machine의 이러한 문제점을 해결하기 위하여 Zhu는 3개 이상의 부류를 갖는 데이터의 패턴 분류를 위하여 Import Vector Machine을 제안하였다. 이 모형은 Support Vector Machine을 이용하여 해결하기 어려운 다중 패턴 분류를 가능케 한다. Import Vector Machine은 커널 로지스틱 기반의 함수만을 사용하지만 본 논문에서는 다수의 커널 함수를 적용하여 가장 성능이 우수한 커널 함수를 찾아내어 최종 분류를 수행하게되는 bagging 기법을 적용하였다 제안하는 방법이 기존의 방법에 비해, 더욱 정확한 분류를 수행함을 실험 결과를 통해 확인한다.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.677-684
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• 2014

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. To improve the estimation performance of SVQR we propose to use SVQR ensemble with bagging (bootstrap aggregating), in which SVQRs are trained independently using the training data sets sampled randomly via a bootstrap method. Then, they are aggregated to obtain the estimator of the quantile regression function using the penalized objective function composed of check functions. Experimental results are then presented, which illustrate the performance of SVQR ensemble with bagging.

• Computers and Concrete
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• v.24 no.1
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• pp.7-17
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• 2019

The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.