• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.5
• /
• pp.525-530
• /
• 2006

This paper presents an efficient input variable selection method using both fixed-point independent component analysis(FP-ICA) and adaptive partition mutual information(AP-MI) estimation. FP-ICA which is based on secant method, is applied to quickly find the independence between input variables. AP-MI estimation is also applied to estimate an accurate dependence information by equally partitioning the samples of input variable for calculating the probability density function(PDF). The proposed method has been applied to 2 problems for selecting the input variables, which are the 7 artificial signals of 500 samples and the 24 environmental pollution signals of 55 samples, respectively The experimental results show that the proposed methods has a fast and accurate selection performance. The proposed method has also respectively better performance than AP-MI estimation without the FP-ICA and regular partition MI estimation.

• Journal of the Korea Society for Simulation
• /
• v.18 no.4
• /
• pp.137-148
• /
• 2009

The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.

• The Korean Journal of Applied Statistics
• /
• v.23 no.2
• /
• pp.295-304
• /
• 2010

Coating thickness is one of target variables in quality control process in steel industry. To predict coating thickness and to control quality of anti-fingerprint steel coils, ultraviolet-visible spectra are measured. We propose a variable-interval selection procedure based on the variable importance in projection in partial least square model. Using the proposed variable interval selection method, prediction performance gets better in the reduced model than the full model with full spectra absorbance. It is also shown that the first differencing as a data preprocessing technique does work well for the prediction of coating thickness.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.169-169
• /
• 2018

국내에서는 유량자료의 부족으로 수공구조물을 설계하기 위한 기초자료로서 설계강우량을 활용하고 있다. 따라서 설계강우량의 산정 및 시간분포가 중요한 요인으로 작용하고 있으며, 국내에서는 설계강우량 시간분포를 위한 방법으로 Huff의 4분위 방법을 사용하는 것이 일반적이다. 실무에서는 확률강우량도 개선 및 보완연구(Ministry of Land, Transport and Maritime Affairs, 2011)에서 제시한 관측소별 Huff의 무차원 누가우량 백분율을 이용하여 Huff의 4분위 방법 중 3분위의 자료를 이용하여 시간분포 회귀식을 산정하고 있으며, 회귀식의 차수는 전반적으로 결정계수가 높은 6차식을 사용하고 있다. 회귀식의 경우 고차식으로 갈수록 결정계수가 높아지는 것은 당연하지만 4차 이상의 회귀식에서는 결정계수의 차이가 미미하므로 6차식을 사용하는 것이 합리적이라고 할 수 없다. 따라서 본 연구에서는 통계적 유의수준에 기초하여 Huff 4분위 방법의 시간분포 회귀식에 대한 유의성 검정을 실시하여 회귀계수에 대한 통계적 검증을 실시하고 변수선택 방법인 전방선택법(Forward Selection)을 이용하여 유의하지 않은 회귀계수들을 제외하면서 가장 좋은 변수들로 구성된 간결한 설계강우량 시간분포 회귀식을 산정하고자 한다. 또한 산정된 회귀식과 기존 확률강우량도 개선 및 보완연구(Ministry of Land, Transport and Maritime Affairs, 2011)에서 제시한 회귀식과 비교하여 변수선택 방법인 전방 선택법(Forward Selection)을 이용하여 산정된 회귀식의 적합성을 검증하고자 한다.

• The Korean Journal of Applied Statistics
• /
• v.24 no.6
• /
• pp.1213-1224
• /
• 2011

In high dimensionality where the number of variables are excessively larger than observations, it is required to remove the noninformative variables to cluster observations. Most model-based approaches for variable selection have been considered under the assumption of homoscedasticity and their models are mainly estimated by a penalized likelihood method. In this paper, a different approach is proposed to remove the noninformative variables effectively and to cluster based on the modified normal mixture model simultaneously. The validity of the model was provided and an EM algorithm was derived to estimate the parameters. Simulation studies and an experiment using real microarray dataset showed the effectiveness of the proposed method.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.3
• /
• pp.533-545
• /
• 2017

Quantile regression models provide a variety of useful statistical information by estimating the conditional quantile function of the response variable. However, the traditional linear quantile regression model can lead to the distorted and incorrect results when analysing real data having a nonlinear relationship between the explanatory variables and the response variables. Furthermore, as the complexity of the data increases, it is required to analyse multiple response variables simultaneously with more sophisticated interpretations. For such reasons, we propose a multivariate quantile regression tree model. In this paper, a new split variable selection algorithm is suggested for a multivariate regression tree model. This algorithm can select the split variable more accurately than the previous method without significant selection bias. We investigate the performance of our proposed method with both simulation and real data studies.

• Journal of Korean Society of Transportation
• /
• v.28 no.4
• /
• pp.119-128
• /
• 2010

Mode choice behavior is associated with travelers' latent behavior that is an unobservable preference to travel behavior or mode characteristics. This paper specifically addresses the problem of unobservable factors, that is latent behavior, in mode choice models. Consideration of latent behavior in mode choice models reduces the errors that come from unobservable factors. In this study, the authors defined the latent variables that mean a quantitative latent behavior factors, and developed the combined RP/SP model with latent variables using the mode choice behavior survey data. The data has traveler's revealed preference of existent modes along the Han River and stated preference of new water transit on the Han River. Also, The data has travelers' latent behavior. Latent variables were defined by factor analysis using the latent behaviour data. In conclusion, it is significant that the relationship between traveler's latent behavior and mode choice behavior. In addition, the goodness-of-fit of the mode choice models with latent variables are better than the model without latent variables.

• Journal of Korean Library and Information Science Society
• /
• v.42 no.4
• /
• pp.243-262
• /
• 2011

The aim of this study was to investigate musculo-skeletal symtoms and working conditions of university library's librarians to search for the risk factors related to musculo-skeletal symptoms. The study subjects were 266 librarians who were working at 20 university libraries. A self-recording questionnaire was used to investigate the general characteristics, working conditions, job intensity, job satisfaction and stress, education of musculoskeletal disorders and nature of musculoskeletal symptom. Statistical analysis was done by using t-test and multiple regression analysis. The complaint proportion of self-reported positive musculoskeletal symptoms was 62.5% and that of severe musculoskeletal symptoms was 26.1%. Multiple regression analysis showed that low satisfaction of working conditions, high job intensity, irregular mealtime, job stress were closely related to the positive rate of musculoskeletal symptoms. Therefore, it will be necessary to make efforts to reduce the prevalence of musculoskeletal disorders improving working conditions and mitigating the job intensity.

• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06a
• /
• pp.334-336
• /
• 2012

EEG 신호에 대한 연구는 의학, 신경과학, 심리학, 컴퓨터과학, 전자 공학 등 여러 학문 분야에서 많은 연구가 진행되고 있다. EEG 신호는 추출하는데 있어서 필연적으로 각종 Artifact와 분석대상이 아닌 신호가 혼재되어 분석 결과의 부정확성을 가지고 있어 EEG 신호의 활용이 주목받은지 오래되었지만 충분히 활용되지 못하고 있다. 이 문제를 해결하기 위해 각종 필터링 연산 등을 통하여 잡음을 제거하고 혼재된 신호를 분류해 내고 있지만, 잡음제거나 신호분류에 사용되는 방법이 고정된 수식을 이용하는 방법이기 때문에 유연한 측정 및 분류를 할 수 없는 것이 현실이다. 본 논문에서 제안하는 매개변수 선택형 시스템은 정제되지 않은 EEG 신호에서 잡파를 제거하고 정제된 신호에서 분석에 필요한 특징을 추출하는데 있어 사용자에게 착용된 EEG 신호 측정기기에서 전극채널, 신호발생영역 및 주파수 대역 등의 매개변수를 선택하고 필요에 따라 매개변수에 가중치를 부여함으로써, 측정의 정확성을 높이고 EEG 신호의 활용에 신뢰도를 향상 시킬 수 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.04a
• /
• pp.288-291
• /
• 2004

기존의 SOFPNN은 데이터 수가 적고 비선형 요소가 많은 시스템에 대한 체계적이고 효율적인 최적 모델 을 구축할 수 있었으며 각 층 노드의 선택 입력을 변화시킴으로써 네트워크 구조 전체의 적응능력을 향상 시켰다. SOFPNN의 구조는 퍼지 다항식 뉴론(FPN)들로 구성되어 있으며, 층이 진행하는 동안 모델 스스로 노드의 선택과 제거를 통해 최적의 네트워크 구조를 생성할 수 있는 유연성을 가지고 있다. 그러나, 노드의 입력변수의 수와 규칙 후반부 다항식 차수 그리고 입력변수는 설계자의 경험 또는 반복적인 학습을 통해 선호된 네트워크 구조를 선택하였으나, 최적의 네트워크 구조를 구축하는데는 어려옴이 내재되어 있었다. 본 논문에서는 자기구성 퍼지 다항식 뉴럴네트워크(Self-Organizing Fuzzy Polynomial Neural Networks： SOFPNN)을 최적화시키기 위해 유전자 알고리즘을 이용하여 자기구성 퍼지 다항식 뉴럴 네트워크의 입력변수의 수와 이에 해당되는 입력변수 그리고 규칙 후반부 다항식의 차수를 탐색하여 최적 의 자기구성 퍼지 다항식 뉴럴 네트워크를 구축한다. 따라서 모델 구축에 있어서 유연성과 정확성을 가지며 객관적이고 좀 더 정확한 예측 능력을 가진 SOFPNN 모델 구조를 구축할 수가 있다.