• Journal of Chest Surgery
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• 제49권sup1호
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• pp.14-19
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• 2016

Background: This study uses the relevance index to understand the condition of regional medical service use for cardiovascular surgery and to identify the medical service use imbalance between regions. Methods: This study calculated the relevance index of 16 metropolitan cities and provinces using resident registration address data from the Ministry of Government Administration and Home Affairs and the 2010-2014 health insurance, medical care assistance, and medical benefits claims data from the Health Insurance Review and Assessment Service. We identified developments over the 5-year time period and analyzed the level of regional imbalance regarding cardiovascular surgery through the relative comparison of relevance indexes between cardiovascular and other types of surgery. Results: The relevance index was high in large cities such as Seoul, Daegu, and Gwangju, but low in regions that were geographically far from the capital area, such as the Gangwon and Jeju areas. Relevance indexes also fell as the years passed. Cardiovascular surgery has a relatively low relevance index compared to key types of surgery of other fields, such as neurosurgery and colorectal surgery. Conclusion: This study identified medical service use imbalance between regions for cardiovascular surgery. Results of this study demonstrate the need for political intervention to enhance the accessibility of necessary special treatment, such as cardiovascular surgery.

• 스마트미디어저널
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• 제8권2호
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• pp.46-57
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• 2019

In recent research, deep learning-based methods have achieved state-of-the-art performance in various computer vision tasks. However, these methods are commonly supervised, and require huge amounts of annotated data to train. Acquisition of data demands an additional costly effort, particularly for the tasks where it becomes challenging to obtain large amounts of data considering the time constraints and the requirement of professional human diligence. In this paper, we present a data level synthetic sampling solution to learn from small and imbalanced data sets using Generative Adversarial Networks (GANs). The reason for using GANs are the challenges posed in various fields to manage with the small datasets and fluctuating amounts of samples per class. As a result, we present an approach that can improve learning with respect to data distributions, reducing the partiality introduced by class imbalance and hence shifting the classification decision boundary towards more accurate results. Our novel method is demonstrated on a small dataset of 2789 tomato plant disease images, highly corrupted with class imbalance in 9 disease categories. Moreover, we evaluate our results in terms of different metrics and compare the quality of these results for distinct classes.

• Safety and Health at Work
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• 제10권2호
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• pp.205-212
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• 2019

Background: Workforce health is one of the primary and most challenging issues, particularly in industrialized countries. This article aims at modeling the major factors affecting accidents in the workplace, including general health, work-family conflict, effort-reward imbalance, and internal and external locus of control. Methods: A cross-sectional study was conducted in Esfahan Steel Company in Iran. A total of 450 participants were divided into two groups-control and case-and the questionnaires were distributed among them. Data were collected through a 7-part questionnaire. Finally, the results were analyzed using SPSS 22.0 and Amos software. Results: All the studied variables had a significant relationship with the accident proneness. In the case group, general health with a coefficient of -0.37, worke-family conflict with 0.10, effort-reward imbalance with 0.10, internal locus of control with -0.07, and external locus of control with 0.40 had a direct effect on occupational stress. Occupational stress also had a positive direct effect on accident proneness with a coefficient of 0.47. In addition, fitness indices of control group showed general health (-0.35), worke-family conflict (0.36), effort-reward imbalance (0.13), internal locus of control (-0.15), and external locus of control (0.12) have a direct effect on occupational stress. Besides, occupational stress with a coefficient of 0.09 had a direct effect on accident proneness. Conclusion: It can be concluded that although previous studies and the present study showed the effect of stress on accident and accident proneness, some hidden and external factors such as work-family conflict, effort-reward imbalance, and external locus of control that affect stress should also be considered. It helps industries face less occupational stress and, consequently, less occurrence rates of accidents.

• 한국콘텐츠학회논문지
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• 제18권8호
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• pp.525-535
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• 2018

분류 문제에서 특정 범주의 빈도가 다른 범주에 비해 과도하게 높은 경우, 왜곡된 기계 학습을 유발할 수 있는 데이터 불균형(imbalanced data) 문제가 발생한다. 기업부도 예측 문제도 그 중 하나인데, 일반적으로 금융기관과 거래하는 기업들의 부도율은 대단히 낮아서, 부도 사례보다 정상 사례의 빈도가 월등히 높은 데이터 불균형 문제가 발생하고 있다. 이러한 데이터 불균형 문제를 해결하기 위해서는 적절한 표본추출 기법이 적용될 필요가 있으며, 지금껏 소수 범주 데이터를 복원 추출함으로써 다수 범주 데이터와 비율을 맞추어 데이터 불균형을 해결하는 오버 샘플링(oversampling) 기법이 주로 활용되어 왔다. 그러나 전통적인 오버 샘플링은 과적합화(overfitting)가 발생할 위험이 높아질 수 있는 단점이 있다. 이러한 배경에서 본 연구는 효과적인 기업부도 예측 모형 학습을 위한 표본추출 기법으로 2014년에 Menardi와 Torelli가 제안한 ROSE(random over sampling examples) 기법을 제안한다. ROSE 기법은 학습에 사용될 사례를 반복적으로 새롭게 합성하여 생성(synthetic generation)하는 기법으로, 과적합화 문제를 회피하면서도 분류 예측 정확도 개선에 도움을 줄 수 있다. 이에 본 연구에서는 ROSE 기법을 가장 성능이 우수한 이분류기로 알려진 SVM(support vector machine)과 결합하여 국내 한 대형 은행의 기업부도 예측에 적용해 보고, 다른 표본추출 기법들과의 비교연구를 수행하였다. 실험 결과, ROSE 기법이 다른 기법에 비해 통계적으로 유의한 수준으로 SVM의 예측정확도 개선에 기여할 수 있음을 확인하였다. 이러한 본 연구의 결과는 부도예측 외에 다른 사회과학 분야 예측문제의 데이터 불균형 문제 해결에도 ROSE가 우수한 대안이 될 수 있다는 사실을 시사한다.

• 디지털산업정보학회논문지
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• 제15권3호
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• pp.197-206
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• 2019

Telemarketing has been used in finance due to the reduction of offline channels. In order to select telemarketing target customers, various machine learning techniques have emerged to maximize the effect of minimum cost. However, there are problems that the class imbalance, which the number of marketing success customers is smaller than the number of failed customers, and the recall rate is lower than accuracy. In this paper, we propose a method that solve the imbalanced class problem and increase the recall rate to improve the efficiency. The hybrid sampling method is applied to balance the data in the class, and the stacked deep network is applied to improve the recall and precision as well as the accuracy. The proposed method is applied to actual bank telemarketing data. As a result of the comparison experiment, the accuracy, the recall, and the precision is improved higher than that of the conventional methods.

• 산업경영시스템학회지
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• 제45권4호
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• pp.86-98
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• 2022

Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manufacturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection methodology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases.

• 통합자연과학논문집
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• 제17권1호
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• pp.1-12
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• 2024

Many abnormal climate events are occurring around the world. The cause of abnormal climate is related to temperature. Factors that affect temperature include excessive emissions of carbon and greenhouse gases from a global perspective, and air circulation from a local perspective. Due to the air circulation, many abnormal climate phenomena such as abnormally high temperature and abnormally low temperature are occurring in certain areas, which can cause very serious human damage. Therefore, the problem of abnormal temperature should not be approached only as a case of climate change, but should be studied as a new category of climate crisis. In this study, we proposed a model for the classification of abnormal temperature using random forests based on various meteorological data such as longitudinal observations, yellow dust, ultraviolet radiation from 2018 to 2022 for each region in Korea. Here, the meteorological data had an imbalance problem, so the imbalance problem was solved by oversampling. As a result, we found that the variables affecting abnormal temperature are different in different regions. In particular, the central and southern regions are influenced by high pressure (Mainland China, Siberian high pressure, and North Pacific high pressure) due to their regional characteristics, so pressure-related variables had a significant impact on the classification of abnormal temperature. This suggests that a regional approach can be taken to predict abnormal temperatures from the surrounding meteorological environment. In addition, in the event of an abnormal temperature, it seems that it is possible to take preventive measures in advance according to regional characteristics.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권12호
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• pp.489-498
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• 2022

최근 노동 집약적인 성격의 섬유 산업에서는 인공지능을 통해 섬유 방사 공정에 들어가는 비용을 줄이고 품질을 최적화하려고 시도 하고 있다. 그러나 섬유 방사 공정은 데이터 수집에 필요한 비용이 크고 체계적인 데이터 수집 및 처리 시스템이 부족하여 축적된 데이터양이 적다. 또 방사 목적에 따라 특정한 변수에만 변화를 준 데이터만을 우선으로 수집하여 데이터 불균형이 발생하며, 물성 측정 환경의 차이로 인해 동일 방사 조건에서 수집된 샘플 간에도 오차가 존재한다. 이러한 데이터 특성들을 고려하지 않고 인공지능 모델에 활용할 경우 과적합과 성능 저하 등의 문제가 발생할 수 있다. 따라서 본 논문에서는 방사 공정 데이터 특성을 고려한 이상치 처리 기법과 데이터 증강 기법을 제안한다. 그리고 이를 기존 이상치 처리 기법 및 데이터 증강 기법과 비교하여 제안한 기법이 방사 공정 데이터에 더 적합함을 보인다. 또 원본 데이터와 제안한 기법들로 처리된 데이터를 다양한 모델에 적용하여 비교함을 통해 제안한 기법들을 사용한 모델들이 그렇지 않은 모델들에 비해 인장 강신도 예측 모델의 성능이 개선됨을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5594-5615
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• 2019

Malicious social robots, which are disseminators of malicious information on social networks, seriously affect information security and network environments. The detection of malicious social robots is a hot topic and a significant concern for researchers. A method based on classification has been widely used for social robot detection. However, this method of classification is limited by an unbalanced data set in which legitimate, negative samples outnumber malicious robots (positive samples), which leads to unsatisfactory detection results. This paper proposes the use of generative adversarial networks (GANs) to extend the unbalanced data sets before training classifiers to improve the detection of social robots. Five popular oversampling algorithms were compared in the experiments, and the effects of imbalance degree and the expansion ratio of the original data on oversampling were studied. The experimental results showed that the proposed method achieved better detection performance compared with other algorithms in terms of the F1 measure. The GAN method also performed well when the imbalance degree was smaller than 15%.

• 한국산학기술학회논문지
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• 제18권1호
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• pp.431-440
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• 2017

본 연구는 아헹가 요가 프로그램이 중년여성의 하체불균형에 미치는 영향을 분석하는데 그 목적이 있다. 연구의 대상자는 35-60세 사이의 중년여성으로서 요가 수련의 경험이 없으며 다른 운동 훈련에 참가 하지 않고, X-RAY검사와 간스테드 테크닉(Gonsted Technique) 분석을 통하여 골반불균형이면서 하지 길이의 차이가 있는 중년여성 24명을 선정하여 12주 동안, 주3회, 1일 90분 동안 수련하였다. 통계방법은 대응 t-검정을 실시하여 수련 전과 후를 비교하였고, 유의 수준은 0.05로 설정하였다. 이 연구의 결과는 첫째, 아헹가 요가는 골반 불균형을 교정하는데 통계적으로 유의한 결과를 나타냈다. 즉 골반 불균형 개선에서 좌 우 엉덩뼈 높이(p < 0.001), 좌 우 엉덩뼈 넓이 (p < 0.001), 좌 우 엉덩뼈 길이 ((p < 0.001), 좌 우 엉치뼈넓이 (p < 0.001)에서 수련 전보다 수련 후 그 차이가 감소하는 유의한 변화를 보여주었다. 둘째, 하지 길이의 변화에서는 아헹가 요가 수련 전 보다 수련 후에 좌 우 하지 길이 차이 (p < 0.001)가 현저하게 감소하였으며 통계적으로 유의한 효과를 나타내었다. 이상의 연구 결과에서 아헹가 요가 수련이 중년여성의 신체불균형을 교정하는데 매우 효과가 크다는 것을 알 수 있었다.