• 제목/요약/키워드: Ensemble network

검색결과 192건 처리시간 0.023초

인공신경망 앙상블을 이용한 옵션 투자예측 시스템 (A Forecasting System for KOSPI 200 Option Trading using Artificial Neural Network Ensemble)

  • 이재식;송영균;허성회
    • 한국지능정보시스템학회:학술대회논문집
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    • 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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    • pp.489-497
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    • 2000
  • After IMF situation, the money market environment is changing rapidly. Therefore, many companies including financial institutions and many individual investors are concerned about forecasting the money market, and they make an effort to insure the various profit and hedge methods using derivatives like option, futures and swap. In this research, we developed a prototype of forecasting system for KOSPI 200 option, especially call option, trading using artificial neural networks(ANN), To avoid the overfitting problem and the problem involved int the choice of ANN structure and parameters, we employed the ANN ensemble approach. We conducted two types of simulation. One is conducted with the hold signals taken into account, and the other is conducted without hold signals. Even though our models show low accuracy for the sample set extracted from the data collected in the early stage of IMF situation, they perform better in terms of profit and stability than the model that uses only the theoretical price.

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투자와 수출 및 환율의 고용에 대한 의사결정 나무, 랜덤 포레스트와 그래디언트 부스팅 머신러닝 모형 예측 (Investment, Export, and Exchange Rate on Prediction of Employment with Decision Tree, Random Forest, and Gradient Boosting Machine Learning Models)

  • 이재득
    • 무역학회지
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    • 제46권2호
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    • pp.281-299
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    • 2021
  • This paper analyzes the feasibility of using machine learning methods to forecast the employment. The machine learning methods, such as decision tree, artificial neural network, and ensemble models such as random forest and gradient boosting regression tree were used to forecast the employment in Busan regional economy. The following were the main findings of the comparison of their predictive abilities. First, the forecasting power of machine learning methods can predict the employment well. Second, the forecasting values for the employment by decision tree models appeared somewhat differently according to the depth of decision trees. Third, the predictive power of artificial neural network model, however, does not show the high predictive power. Fourth, the ensemble models such as random forest and gradient boosting regression tree model show the higher predictive power. Thus, since the machine learning method can accurately predict the employment, we need to improve the accuracy of forecasting employment with the use of machine learning methods.

Connection Machine CM-2상에서 신경망군(群)의 병렬 구현 (Parallel Implementation of A Neural Network Ensemble on the Connection Machine CM-2)

  • 김대진
    • 전자공학회논문지C
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    • 제34C권1호
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    • pp.28-41
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    • 1997
  • This paper describes a parallel implementation of a neurla network ensemble developed for object recognition on the connection machine CM-2. The implementation ensures that multiple networks are implemented simultaneously starting from different initial weights and all training samples are applied to each network by one sample per a copy of each network. When compared with a sequential implementation, this accelerates the computation speed by O(N.m.n) where N, m, and n are the network, respectively. The speedup in the computation time and the convergence characteristics of sthe modified backpropagation learning precedure were evaluated by two-dimensional object recognition problem.

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트래픽 데이터의 통계적 기반 특징과 앙상블 학습을 이용한 토르 네트워크 웹사이트 핑거프린팅 (Tor Network Website Fingerprinting Using Statistical-Based Feature and Ensemble Learning of Traffic Data)

  • 김준호;김원겸;황두성
    • 정보처리학회논문지:소프트웨어 및 데이터공학
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    • 제9권6호
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    • pp.187-194
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    • 2020
  • 본 논문은 클라이언트의 익명성과 개인 정보를 보장하는 토르 네트워크에서 앙상블 학습을 이용한 웹사이트 핑거프린팅 방법을 제안한다. 토르네트워크에서 수집된 트래픽 패킷들로부터 웹사이트 핑거프린팅을 위한 훈련 문제를 구성하며, 트리 기반 앙상블 모델을 적용한 웹사이트 핑거프린팅 시스템의 성능을 비교한다. 훈련 특징 벡터는 트래픽 시퀀스에서 추출된 범용 정보, 버스트, 셀 시퀀스 길이, 그리고 셀 순서로부터 준비하며, 각 웹사이트의 특징은 고정 길이로 표현된다. 실험 평가를 위해 웹사이트 핑거프린팅의 사용에 따른 4가지 학습 문제(Wang14, BW, CWT, CWH)를 정의하고, CUMUL 특징 벡터를 사용한 지지 벡터 기계 모델과 성능을 비교한다. 실험 평가에서, BW 경우를 제외하고 제안하는 통계 기반 훈련 특징 표현이 CUMUL 특징 표현보다 우수하다.

동적 중요도 결정 방법을 이용한 새로운 앙상블 시스템 (A New Ensemble System using Dynamic Weighting Method)

  • 서동훈;이원돈
    • 한국정보통신학회논문지
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    • 제15권6호
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    • pp.1213-1220
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    • 2011
  • 본 논문에서는 분류자들 속에 중요도 정보를 삽입하여 동적 중요도 결정이 가능한 앙상블 시스템을 제안하였다. 그동안 앙상블 시스템에서 중요도는 훈련이 끝나고 결정된 중요도를 사용하였다. 한 번 결정된 중요도는 테스트 데이터에 상관없이 정적으로 사용되었다. 이 문제를 푸는 방법으로 관문 네트워크에서 구조적으로 계층을 두는 프로세스를 추가하여 동적 중요도 결정이 가능하게 하는 방법이 있지만 프로세스가 추가된다는 단점이 있다. 본 논문에서는 이런 추가적인 프로세스 없이 간단하게 동적 중요도 결정이 가능한 방법을 보여주고 구조적 변경 없이 기존의 시스템에 쉽게 적용할 수 있으며 AdaBoost보다 나은 성능을 보여주는 알고리즘을 제안한다.

대용량 이미지넷 인식을 위한 CNN 기반 Weighted 앙상블 기법 (CNN-based Weighted Ensemble Technique for ImageNet Classification)

  • 정희철;최민국;김준광;권순;정우영
    • 대한임베디드공학회논문지
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    • 제15권4호
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    • pp.197-204
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    • 2020
  • The ImageNet dataset is a large scale dataset and contains various natural scene images. In this paper, we propose a convolutional neural network (CNN)-based weighted ensemble technique for the ImageNet classification task. First, in order to fuse several models, our technique uses weights for each model, unlike the existing average-based ensemble technique. Then we propose an algorithm that automatically finds the coefficients used in later ensemble process. Our algorithm sequentially selects the model with the best performance of the validation set, and then obtains a weight that improves performance when combined with existing selected models. We applied the proposed algorithm to a total of 13 heterogeneous models, and as a result, 5 models were selected. These selected models were combined with weights, and we achieved 3.297% Top-5 error rate on the ImageNet test dataset.

흉부 X-선 영상을 이용한 14 가지 흉부 질환 분류를 위한 Ensemble Knowledge Distillation (Ensemble Knowledge Distillation for Classification of 14 Thorax Diseases using Chest X-ray Images)

  • 호티키우칸;전영훈;곽정환
    • 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.

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Transfer Learning-Based Feature Fusion Model for Classification of Maneuver Weapon Systems

  • Jinyong Hwang;You-Rak Choi;Tae-Jin Park;Ji-Hoon Bae
    • Journal of Information Processing Systems
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    • 제19권5호
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    • pp.673-687
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    • 2023
  • Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.

현재 기상 정보의 이동 평균을 사용한 태양광 발전량 예측 (Use of the Moving Average of the Current Weather Data for the Solar Power Generation Amount Prediction)

  • 이현진
    • 한국멀티미디어학회논문지
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    • 제19권8호
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    • pp.1530-1537
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    • 2016
  • Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

네트워크 트래픽 수집 및 복원을 통한 내부자 행위 분석 프레임워크 연구 (A Study on the Insider Behavior Analysis Framework for Detecting Information Leakage Using Network Traffic Collection and Restoration)

  • 고장혁;이동호
    • 디지털산업정보학회논문지
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    • 제13권4호
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    • pp.125-139
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    • 2017
  • In this paper, we developed a framework to detect and predict insider information leakage by collecting and restoring network traffic. For automated behavior analysis, many meta information and behavior information obtained using network traffic collection are used as machine learning features. By these features, we created and learned behavior model, network model and protocol-specific models. In addition, the ensemble model was developed by digitizing and summing the results of various models. We developed a function to present information leakage candidates and view meta information and behavior information from various perspectives using the visual analysis. This supports to rule-based threat detection and machine learning based threat detection. In the future, we plan to make an ensemble model that applies a regression model to the results of the models, and plan to develop a model with deep learning technology.