• 응용통계연구
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• 제24권4호
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• pp.587-595
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• 2011

Credit scoring is an objective and automatic system to assess the credit risk of each customer. The logistic regression model is one of the popular methods of credit scoring to predict the default probability; however, it may not detect possible nonlinear features of predictors despite the advantages of interpretability and low computation cost. In this paper, we propose to use a generalized partially linear model as an alternative to logistic regression. We also introduce modern ensemble technologies such as bagging, boosting and random forests. We compare these methods via a simulation study and illustrate them through a German credit dataset.

• Genomics & Informatics
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• 제5권1호
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• pp.19-23
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• 2007

In this paper, we provide an overview of statistical models for haplotype-based association studies, and summarize their features based on the design matrix. We classify the design matrix into the two types: direct and indirect. For these two kinds of matrices, we present and compare characteristics using a simple hypothetical example, and a real data set. The motivation behind this study was to provide practitioners with an improved understanding, to facilitate the informed selection of the appropriate haplotype-based model and to improve the interpretability of the models.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.829-837
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• 2011

본 논문에서는 비관측요인모형을 이용하여 한국의 국내총생산 시계열 자료를 분석한다. 이 모형이 확률적 및 결정적 요인들을 모두 포괄할 수 있다는 점을 이용하여, 보다 다양한 형태로 시계열 자료의 모형화를 시도하였으며, 지수평활법 및 박스-젠킨스의 ARIMA모형과 예측력을 비교하였다. 국내 총생산 자료에 대한 2년간의 미래 예측에서 비관측요인모형이 보다 우수함을 보인다.

• Computers and Concrete
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• 제32권3호
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• 전기학회논문지
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• 제61권1호
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• pp.135-142
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• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2314-2333
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• 2023

It is significant to predict the performance degradation of complex electromechanical systems. Among the existing performance degradation prediction models, belief rule base (BRB) is a model that deal with quantitative data and qualitative information with uncertainty. However, when analyzing dynamic systems where observable indicators change frequently over time and working conditions, the traditional belief rule base (BRB) can not adapt to frequent changes in working conditions, such as the prediction of aeroengine performance degradation considering working condition. For the sake of settling this problem, this paper puts forward a new hidden belief rule base (HBRB) prediction method, in which the performance of aeroengines is regarded as hidden behavior, and operating conditions are used as observable indicators of the HBRB model to describe the hidden behavior to solve the problem of performance degradation prediction under different times and operating conditions. The performance degradation prediction case study of turbofan aeroengine simulation experiments proves the advantages of HBRB model, and the results testify the effectiveness and practicability of this method. Furthermore, it is compared with other advanced forecasting methods. The results testify this model can generate better predictions in aspects of accuracy and interpretability.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.731-738
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• 2011

In this paper, we introduce an advanced architecture of K-Means clustering-based polynomial Radial Basis Function Neural Networks (p-RBFNNs) designed with the aid of SSOA (Space Search Optimization Algorithm) and develop a comprehensive design methodology supporting their construction. In order to design the optimized p-RBFNNs, a center value of each receptive field is determined by running the K-Means clustering algorithm and then the center value and the width of the corresponding receptive field are optimized through SSOA. The connections (weights) of the proposed p-RBFNNs are of functional character and are realized by considering three types of polynomials. In addition, a WLSE (Weighted Least Square Estimation) is used to estimate the coefficients of polynomials (serving as functional connections of the network) of each node from output node. Therefore, a local learning capability and an interpretability of the proposed model are improved. The proposed model is illustrated with the use of nonlinear function, NOx called Machine Learning dataset. A comparative analysis reveals that the proposed model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• 대한교통학회지
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• 제4권1호
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• pp.12-27
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• 1986

This study examined user preferences toward transportation modes in Seoul. Two multidimensional scaling models, the ideal point and vector models, were applied to data on mode preferences of 114 adults in the metropolitan area. While both models produced fairly similar results, the vector model performed slightly better than the other in terms of interpretability of the results. The transport attributes elicited are comfort, flexibility, travel cost, travel time, privacy, and safety; among which comfort is salient most. The comfort variable is a multi-faceted attribute in nature. The variations of attribute preferences are most significant between the gender groups as well as worker/nonworker groups. In particular, male workers, female workers and female nonworkers form three distinctive market segments. An unidimensional scaling of the preference data reveals that subway, auto-driver, and subscription bus modes are preferred most, whereas motorcycle and bicycle least. The other modes of express bus, taxt, auto-passenger, bus and walk rank intermediately. An examination of how preference orders vary among modal groups hints that users align their stated attitudes to their choice in order to reduce cognitive dissonance.

• 응용통계연구
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• 제25권4호
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• pp.613-620
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• 2012

In most cases of multidimensional scaling(MDS), the distances or dissimilarities among units are assumed to be symmetric. Thus, it is not an easy task to deal with asymmetric distances. Asymmetric MDS developed so far face difficulties in the interpretation of results. This study proposes a much simpler asymmetric MDS, that utilizes the notion of "altitude". The analogy arises in mountaineering: It is easier (more difficult) to move from the higher (lower) point to the lower (higher). The idea is formulated as a quantification problem, in which the disparity of distances is maximally related to the altitude difference. The proposed method is demonstrated in three examples, in which the altitudes are visualized by rainbow colors to ease the interpretability of users.

• Korean Chemical Engineering Research
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• 제61권4호
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• pp.542-549
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• 2023

대안 모델링에 대한 관심이 커진 이후 데이터 기반의 기계학습을 이용하여 비선형 화학 공정을 모사하고자 하는 연구가 지속되고 있다. 그러나 기계 학습 모델의 black box 성질로 인하여 모델의 해석 가능성에 한계는 산업 적용에 걸림돌이 되고 있다. 따라서, 모델의 정확도가 보장된 상태에서 해석력을 부여하는 개념인 설명 가능한 인공지능(explainable artificial intelligence, XAI)을 이용하여 화학 공정 분석을 시도하고자 한다. 기존의 화학 공정 민감도 분석이 변수의 민감도 지수를 계산하고 순위를 매기는 데에 그쳤다면, XAI를 이용하여 전역적, 국소적 민감도 분석뿐만 아니라 변수들 간의 상호작용에 대하여 분석하여 데이터로부터 물리적 통찰을 얻어내는 방법론을 제안한다. 사례 연구의 대상공정인 암모니아 합성 공정에 대하여 첫번째 반응기로 향하는 흐름에 대한 예열기(preheater)의 온도, 세 반응기로 향하는 cold-shot의 분배 비율을 공정 변수로 설정하였다. Matlab과 Aspen plus를 연동하여 공정 변수를 바꿔가면서 암모니아의 생산량과 세 반응기의 최고 온도에 대한 데이터를 얻었으며, tree 기반의 모델들을 훈련시켰다. 그리고 성능이 좋은 모델에 대하여 XAI 기법 중 하나인 SHAP 기법을 이용하여 민감도 분석을 수행하였다. 전역적 민감도 분석 결과, 예열기의 온도가 가장 큰 영향을 미쳤으며 국소적 민감도 분석 결과에서 생산성 향상 및 과열 방지를 위한 공정 변수들의 범위를 규정할 수 있었다. 이처럼 화학 공정의 대안 모델을 구축하고 설명 가능한 인공지능을 이용해 민감도 분석을 진행하는 방법론을 통해 공정 최적화에 대한 정량적, 정성적 피드백을 제안하는 데 도움을 줄 것이다.