• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.597-602
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• 2007

데이터 마이닝 분야에서 앙상블 모형의 유용성은 널리 인정되고 있다. 앙상블을 구성하는 단위모형들 사이의 다양성이 보장되는 경우, 최종 모형의 정확성 및 안정성이 향상되기 때문이다. 하지만, 얼마나 많은 단위 모형들이 어떤 방식으로 결합되어야 하는가에 대해서는 아직도 더 많은 연구가 필요하다. 본 연구에서는 신용카드 부정사용 유형 중 하나인 현금불법융통 문제에 대해 앙상블 모형의 유용성을 검증하고자 한다. 부정행위 적발 모형은 전형적인 분류 문제의 한 유형이나, 클래스간 불균형이 매우 심하다는 특징이 있다. 따라서, 현금불법융통 문제에 적합한 다양성(Diversity) 척도를 개발하여 최소한의 단위모형들로 앙상블 모형을 구성하는 방안을 제시하였다. 축소된 앙상블 모형이 많은 수의 모형을 결합한 앙상블 모형과 거의 같은 정확성 및 안정성을 보임을 국내 신용카드사의 실제 자료를 사용하여 입증하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.434-436
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• 2003

DNA칩 기술로 얻어지는 유전자발현데이터(gene expression data)는 생채 조직이나 세포의 수천개에 달하는 유전자의 발현량(expression level)을 측정한 것으로, 유전자발현양상(gene expression pattern)에 기반한 암 종류의 분류 등에 유용하다. 본 논문에서는 확률그래프모델(probabilistic graphical model)의 하나인 베이지안망(Bayesian network)을 발현데이터의 분류에 적응하며, 분류 성능을 높이기 위해 베이지안망의 앙상블(ensemble of Bayesian networks)을 구성한다. 실험은 실제 암 조직에서 추출된 유전자발현데이터에 대해 행해졌다 실험 결과, 앙상블 베이지안망의 분류 정확도는 단일 베이지안망보다 높았으며, naive Bayes 분류기, 신경망, support vector machine(SVM) 등과 대등한 성능을 보였다.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.2
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• pp.139-146
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• 2020

The camouflage pattern was difficult to distinguish from the surrounding background, so it was difficult to classify the object and the background image when the color image is used as the training data of deep-learning. In this paper, we proposed a red-green color blindness image transformation method using the principle that people of red-green blindness distinguish green color better than ordinary people. Experimental results show that the camouflage soldier's recognition performance improved by proposed a deep learning model of the ensemble technique using the imitated red-green-blind image data and the original color image data.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1377-1382
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• 2018

In the marine environment, many clutters have similar features with the marine targets due to the diverse changes of the air temperature, water temperature, various weather and seasons. Also, the clutters in the ground environment have similar features due to the same reason. In this paper, we proposed a robust Hybrid Machine Character (HMC) method to classify the targets from the clutters in the infrared images for the various environments. The proposed HMC method adopts human's multiple personality utilization and the CNN ensemble method to classify the targets in the ground and marine environments. This method uses an advantage of the each environmental training model. Experimental results demonstrate that the proposed method has better success rate to classify the targets and clutters than previously proposed CNN classification method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.872-875
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• 2009

In a bankruptcy prediction model, the accuracy is one of crucial performance measures due to its significant economic impacts. Ensemble is one of widely used methods for improving the performance of classification and prediction models. Two popular ensemble methods, Bagging and Boosting, have been applied with great success to various machine learning problems using mostly decision trees as base classifiers. In this paper, we analyze the performance of boosted neural networks for improving the performance of traditional neural networks on bankruptcy prediction tasks. Experimental results on Korean firms indicated that the boosted neural networks showed the improved performance over traditional neural networks.

• Bulletin of the Korean Chemical Society
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• v.21 no.3
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• pp.310-316
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• 2000

Molecular dynamics simulation studies for thermotropic liquid crystalline systems conposed of rodlike molecules with 6 Lennard-Jones interaction sites wre performed in NPT ensemble. Within the range of temperature studied, the system exhibited isotropic and smectic phase. For the characterization of the smectic phase, we examined the structure of the liquid crystalline phase via the radial distribution function, its longitudinal and transverse components to the director, and other orientational correlation function, its longitudinal and transverse components to the director, and other orientational correlation functions. In the smectic A phase, our results showed a large anisotropy in translational motion (i.e.,$D_⊥ >> D_∥$), and the decay of the collective orientational correlation function of rank two became slower than that of the single particle orientational correlation function of rank one. Comments on the spontaneous growth of orientational order directly from the isotropic phase are given.

• Smart Media Journal
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• v.12 no.10
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• pp.9-18
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• 2023

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.681-684
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• 2023

우리나라의 수소연료전지 차량의 점유율이 매년 증가하고 있으나, 수소충전소 설비의 잦은 중단으로 수소연료전지 차량 운전자들이 제때 차량을 충전하지 못하는 불편이 발생하고 있다. 본 논문에서는 수소충전소 설비 중 Diaphragm을 사용하는 압축기의 이상 패턴을 탐지하는 Ensemble 모델을 통해 수소충전소에서 2023년 1월 1일부터 2023년 6월 28일 동안 수집된 데이터를 분석하였으며, 해당 기간 동안 발생했던 고장에 대해 2일전부터 이상 패턴이 10,000 이상 탐지되는 결과를 얻었다.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.1-12
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• 2023

Ensuring the security of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) is paramount to safeguarding the reliability and safety of critical infrastructure. This paper addresses the significant threat posed by reconnaissance attacks on SCADA/ICS networks and presents an innovative methodology for enhancing their protection. The proposed approach strategically employs imbalance dataset handling techniques, ensemble methods, and feature engineering to enhance the resilience of SCADA/ICS systems. Experimentation and analysis demonstrate the compelling efficacy of our strategy, as evidenced by excellent model performance characterized by good precision, recall, and a commendably low false negative (FN). The practical utility of our approach is underscored through the evaluation of real-world SCADA/ICS datasets, showcasing superior performance compared to existing methods in a comparative analysis. Moreover, the integration of feature augmentation is revealed to significantly enhance detection capabilities. This research contributes to advancing the security posture of SCADA/ICS environments, addressing a critical imperative in the face of evolving cyber threats.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.383-400
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• 2023

Air pollution-related diseases are escalating worldwide, with the World Health Organization (WHO) estimating approximately 7 million annual deaths in 2022. The rapid expansion of industrial facilities, increased emissions from various sources, and uncontrolled release of odorous substances have brought air pollution to the forefront of societal concerns. In South Korea, odor is categorized as an independent environmental pollutant, alongside air and water pollution, directly impacting the health of local residents by causing discomfort and aversion. However, the current odor management system in Korea remains inadequate, necessitating improvements. This study aims to enhance the odor management system by analyzing 1,010,749 data points collected from odor sensors located in Osong, Chungcheongbuk-do, using an Ensemble-Based Multi-Region Integrated Odor Concentration Prediction Model. The research results demonstrate that the model based on the XGBoost algorithm exhibited superior performance, with an RMSE of 0.0096, significantly outperforming the single-region model (0.0146) with a 51.9% reduction in mean error size. This underscores the potential for increasing data volume, improving accuracy, and enabling odor prediction in diverse regions using a unified model through the standardization of odor concentration data collected from various regions.