• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.467-474
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• 2006

In this paper we present a weighted support vector machine(SVM) and a weighted least squares support vector machine(LS-SVM) for the prediction in the heteroscedastic regression model. By adding weights to standard SVM and LS-SVM the better fitting ability can be achieved when errors are heteroscedastic. In the numerical studies, we illustrate the prediction performance of the proposed procedure by comparing with the procedure which combines standard SVM and LS-SVM and wild bootstrap for the prediction.

• Journal of the Korean Data and Information Science Society
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• 제28권1호
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• pp.227-235
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• 2017

When the spatial information of each location is given specifically as coordinates it is popular to use the geographically weighted regression to incorporate the spatial information by assuming that the regression parameters vary spatially across locations. In this paper, we relax the linearity assumption of geographically weighted regression and propose a geographically weighted least squares-support vector machine for estimating geographically weighted mean by using the basic concept of kernel machines. Generalized cross validation function is induced for the model selection. Numerical studies with real datasets have been conducted to compare the performance of proposed method with other methods for predicting geographically weighted mean.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.999-1005
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• 2011

This paper proposes a weighted least squares support vector machine for asymmetric least squares regression. This method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. The cross validation function is introduced to choose optimal hyperparameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.765-776
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• 2006

In this paper we propose an estimation method on the regression model with randomly censored observations of the training data set. The weighted least squares support vector machine regression is applied for the regression function estimation by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed estimation method.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.161-169
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• 2005

In this paper we propose a fuzzy c-regression model based on weighted least squares support vector machine(LS-SVM), which can be used to detect outliers in the switching regression model while preserving simultaneous yielding the estimates of outputs together with a fuzzy c-partitions of data. It can be applied to the nonlinear regression which does not have an explicit form of the regression function. We illustrate the new algorithm with examples which indicate how it can be used to detect outliers and fit the mixed data to the nonlinear regression models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.5143-5158
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• 2018

Improving the intrusion detection system (IDS) is a pressing need for cyber security world. With the growth of computer networks, there are constantly daily new attacks. Machine Learning (ML) is one of the most important fields which have great contribution to address the intrusion detection issues. One of these issues relates to the imbalance of the diverse classes of network traffic. Accuracy paradox is a result of training ML algorithm with imbalanced classes. Most of the previous efforts concern improving the overall accuracy of these models which is truly important. However, even they improved the total accuracy of the system; it fell in the accuracy paradox. The seriousness of the threat caused by the minor classes and the pitfalls of the previous efforts to address this issue is the motive for this work. In this paper, we consolidated stratified sampling, cost function and weighted Support Vector Machine (WSVM) method to address the accuracy paradox of ID problem. This model achieved good results of total accuracy and superior results in the small classes like the User-To-Remote and Remote-To-Local attacks using the improved version of the benchmark dataset KDDCup99 which is called NSL-KDD.

• 응용통계연구
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• 제21권1호
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• pp.159-168
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• 2008

수익률 기간구조(term structure of interest rates, 이하 수익률곡선)는 자료의 성격이 경시적(longitudinal)이므로 만기까지 기간과 시간을 동시에 입력변수로 고려해야만 유용하고 효율적인 함수추정이 가능하다. 고러나 이러한 방법은 다루어야 하는 자료가 대용량이기 때문에 대용량 자료에 적합하고 실행속도가 빠른 추정기법을 개발하는 것이 필요하다. 한편 자료에 내재하는 자기상관성 구조 때문에 과대 적합된 추정 결과를 얻기 쉽다. 따라서 본 논문에서는 이러한 문제를 해결하기 위해서 가중 LS-SVM(least squares support vector machine, 최소제곱 서포트벡터기계)의 혼합모형을 제안한다. 미국 재무부 채권에 대한 사례연구를 통해서 추정 결과가 증권시장 붕괴 같은 이례적 사건의 현상을 잘 반영하고 있음을 확인할 수 있었다.

• 한국인터넷방송통신학회논문지
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• 제9권2호
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• pp.1-6
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• 2009

많은 양의 데이터로부터 유용성있는 정보의 추출, 진단 및 예후에 대한 결정, 질병 치료의 응용 등은 바이오 인포머틱스(Bioinformatics)분야에서 매우 중요한 문제들이다. 본 논문에서는 암진단시스템에 적용하기위해 support vector machine을 위한 weogjted lernel fuction과 빠른 수렴성과 좋은 분류성능을 갖는 학습방법을 제안하였다. 제안된 kernel function에서 기본적인 kernel fuction의 weights는 암진단 학습단계에서 결정되고 분류단계에서 파리미터로 사용된다. 대장암 데이터와 같은 임상 데이터에 대한 실험결과에서 제안된 방법은 기존의 다른 kernel fuction들 보다 더 우수하고 안정적인 분류성능을 보여주었다.

• Journal of the Korean Data and Information Science Society
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• 제21권5호
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• pp.831-839
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• 2010

최소제곱 서포트벡터기계는 비선형회귀분석과 분류에 널리 쓰이는 커널기법이다. 본 논문에서는 금융시계열자료의 평균 및 변동성을 추정하기 위하여 평균의 추정 방법으로는 가중최소제곱 서포트벡터기계, 변동성의 추정 방법으로는 최소제곱 서포트벡터기계를 사용하는 비선형 평균 일반화 이분산 자기회귀모형을 제안한다. 제안된 모형은 선형 일반화 이분산 자기회귀모형 및 선형 평균 일반화 이분산 자기회귀모형보다 더 나은 추정 능력을 가진다는 것을 실제자료의 추정을 통하여 보였다.

• Computers and Concrete
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• 제18권6호
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• pp.1065-1082
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• 2016

The main aim of this study is to predict the compressive and flexural strengths of self-compacting mortar (SCM) containing $nano-SiO_2$, $nano-Fe_2O_3$ and nano-CuO using wavelet-based weighted least squares-support vector machines (WLS-SVM) approach which is called WWLS-SVM. The WWLS-SVM regression model is a relatively new metamodel has been successfully introduced as an excellent machine learning algorithm to engineering problems and has yielded encouraging results. In order to achieve the aim of this study, first, the WLS-SVM and WWLS-SVM models are developed based on a database. In the database, nine variables which consist of cement, sand, NS, NF, NC, superplasticizer dosage, slump flow diameter and V-funnel flow time are considered as the input parameters of the models. The compressive and flexural strengths of SCM are also chosen as the output parameters of the models. Finally, a statistical analysis is performed to demonstrate the generality performance of the models for predicting the compressive and flexural strengths. The numerical results show that both of these metamodels have good performance in the desirable accuracy and applicability. Furthermore, by adopting these predicting metamodels, the considerable cost and time-consuming laboratory tests can be eliminated.