• 지적과 국토정보
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• 제46권2호
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• pp.43-55
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• 2016

본 연구는 사례 기반 학습(instance-based learning)의 논리를 활용하여 지가를 추정하였다. 다양한 사례 기반 학습 기법 중 k-최근린법을 이용하였으며, k-최근린법 적용시 유사성을 측정하는 거리척도는 유클리디안 거리를 비롯해 문헌에 비교적 자주 등장하는 10개의 거리척도를 사용하였다. 본 연구에서는 k-최근린법에 의한 10 종류의 예측값 중 가장 우수한 성능을 보이는 1개의 예측값을 최종 가격으로 선택하는 대신, 이들 예측값들을 병합하는 앙상블(ensemble) 기법의 논리를 적용하여 최종 예측값을 결정하였다. 앙상블 기법 중 일종의 잔차 적합 모형인 경사 부스팅 앨고리듬을 적용하여 최종 가격을 정하였다. 본 연구에서는 이러한 사례 기반 학습과 앙상블 기법의 이점을 실증적으로 제시하기 위해 전라남도 해남군 소재 농지를 사례로 하여 가격을 추정하였으며, k-최근린법에 의한 10 종류의 예측값보다 앙상블 기법에 의한 가격이 보다 정확한 것을 확인할 수 있었다.

• Computers and Concrete
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• 제32권3호
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• pp.313-326
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• 2023

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.

• Smart Structures and Systems
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• 제29권1호
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• 한국산업정보학회논문지
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• 제17권7호
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• pp.139-148
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• 2012

분류기의 앙상블 학습은 여러 개의 서로 다른 분류기들의 조합을 통해 만들어진다. 앙상블 학습은 기계학습 분야에서 많은 관심을 끌고 있는 중요한 연구주제이며 대부분의 경우에 있어서 앙상블 모형은 개별 기저 분류기보다 더 좋은 성과를 내는 것으로 알려져 있다. 본 연구는 부도 예측 모형의 성능개선에 관한 연구이다. 이를 위해 본 연구에서는 단일 모형으로 그 우수성을 인정받고 있는 SVM을 기저 분류기로 사용하는 앙상블 모형에 대해 고찰하였다. SVM 모형의 성능 개선을 위해 bagging과 random subspace 모형을 부도 예측 문제에 적용해 보았으며 bagging 모형과 random subspace 모형의 성과 개선을 위해 bagging과 random subspace의 통합 모형을 제안하였다. 제안한 모형의 성과를 검증하기 위해 실제 기업의 부도 예측 데이터를 사용하여 실험하였고, 실험 결과 본 연구에서 제안한 새로운 형태의 통합 모형이 가장 좋은 성과를 보임을 알 수 있었다.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권2호
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• pp.135-139
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• 2009

본 논문에서는 지역 전문가를 이용한 새로운 앙상블 방법을 제시하고자 한다. 이 앙상블 방법에서는 학습 데이타를 분할하여 속성 공간의 서로 다른 지역을 이용하여 전문가를 학습시킨다. 새로운 데이타를 분류할 때에는 그 데이타가 속한 지역을 담당하는 전문가들로 가중치 투표를 한다. UCI 기계 학습 데이타 저장소에 있는 10개의 데이타를 이용하여 단일 분류기, Bagging, Adaboost와 정확도를 비교하였다. 학습 알고리즘으로는 SVM, Naive Bayes, C4.5를 사용하였다. 그 결과 지역 전문가의 앙상블 학습 방법이 C4.5를 학습 알고리즘으로 사용한 Bagging, Adaboost와는 비슷한 성능을 보였으며 나머지 분류기보다는 좋은 성능을 보였다.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.3944-3951
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• 2021

Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.

• 한국산업융합학회 논문집
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• 제26권6_3호
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• pp.1205-1213
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• 2023

This study delves into methods for enhancing the power efficiency of air compressor systems, with the primary objective of significantly impacting industrial energy consumption and environmental preservation. The paper scrutinizes Shinhan Airro Co., Ltd.'s power efficiency optimization technology and employs machine learning ensemble models to simulate power efficiency optimization. The results indicate that Shinhan Airro's optimization system led to a notable 23.5% increase in power efficiency. Nonetheless, the study's simulations, utilizing machine learning ensemble techniques, reveal the potential for a further 51.3% increase in power efficiency. By continually exploring and advancing these methodologies, this research introduces a practical approach for identifying optimization points through data-driven simulations using machine learning ensembles.

• Computers and Concrete
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• 제16권5호
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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• pp.313-315
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• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.

• 정보보호학회논문지
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• 제32권2호
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• pp.267-277
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• 2022

최근 클라우드 및 원격 근무 환경의 비중이 증가함에 따라 다양한 정보보안 사고들이 발생하고 있다. 조직의 내부자가 원격 접속으로 기밀 자료에 접근하여 유출을 시도하는 사례가 발생하는 등 내부자 위협이 주요 이슈로 떠오르게 되었다. 이에 따라 내부자 위협을 탐지하기 위해 기계학습 기반의 방법들이 제안되고 있다. 하지만, 기존의 내부자 위협을 탐지하는 기계학습 기반의 방법들은 편향 및 분산 문제와 같이 예측 정확도와 관련된 중요한 요소를 고려하지 않았으며 이에 따라 제한된 성능을 보인다는 한계가 있다. 본 논문에서는 편향 및 분산을 고려하는 부스팅 유형의 앙상블 학습 알고리즘들을 사용하여 악의적인 내부자 탐지 성능을 확인하고 이에 대한 면밀한 분석을 수행하며, 데이터셋의 불균형까지도 고려하여 최종 결과를 판단한다. 앙상블 학습을 이용한 실험을 통해 기존의 단일 학습 모델에 기반한 방법에서 나아가, 편향-분산 트레이드오프를 함께 고려하며 유사하거나 보다 높은 정확도를 달성함을 보인다. 실험 결과에 따르면 배깅과 부스팅 방법을 사용한 앙상블 학습은 98% 이상의 정확도를 보였고, 이는 사용된 단일 학습 모델의 평균 정확도와 비교하면 악의적인 내부자 탐지 성능을 5.62% 향상시킨다.