• KIISE Transactions on Computing Practices
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• v.20 no.10
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• pp.549-554
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• 2014

Handwritten character recognition data is usually imbalanced because it is collected from the natural language sentences written by different writers. The imbalanced data can cause seriously negative effect on the performance of most of machine learning algorithms. But this problem is typically ignored in handwritten character recognition, because it is considered that most of difficulties in handwritten character recognition is caused by the high variance in data set and similar shapes between characters. We propose the oversampling-based ensemble learning methods to solve imbalanced data problem in handwritten character recognition and to improve the recognition accuracy. Also we show that proposed method achieved improvements in recognition accuracy of minor classes as well as overall recognition accuracy empirically.

• Annual Conference of KIPS
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• 2024.05a
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• pp.652-655
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• 2024

COPD(Chronic Obstructive Pulmonary Disease)는 장기간에 걸쳐 기도가 좁아지는 폐질환으로, 규칙적 운동은 호흡을 용이하게 하고 증상을 개선할 수 있는 주요 자가관리 중재법 중 하나이다. 건강정보 데이터와 인공지능을 사용하여 규직적 운동 이행군과 불이행군을 선별하여 자가관리 취약 집단을 파악하는 것은 질병관리 측면에서 비용효과적인 전략이다. 하지만 많은 양의 데이터를 확보하기 어렵고, 규칙적 운동군과 그렇지 않은 환자의 비율이 상이하기 때문에 인공지능 모델의 전체적인 선별 능력을 향상시키기 어렵다는 한계가 있다. 이러한 한계를 극복하기 위해 본 연구에서는 국민건강영양조사 데이터를 사용하여 머신러닝 모델인 XGBoost와 딥러닝 모델인 MLP에 오버샘플링, 언더샘플링, 가중치 부여 등 불균형 데이터 처리 기법을 적용 후 성능을 비교하여 가장 효과적인 불균형 데이터 처리 기법을 제시한다.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.434-439
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• 2019

딥러닝 기반 분류 모델에 있어 데이터의 클래스 불균형 문제는 소수 클래스의 분류 성능을 크게 저하시킨다. 본 논문에서는 앞서 언급한 클래스 불균형 문제를 보완하기 위한 방안으로 적대적 학습 기법을 제안한다. 적대적 학습 기법의 성능 향상 여부를 확인하기 위해 총 4종의 딥러닝 기반 분류 모델을 정의하였으며, 해당 모델 간 분류 성능을 비교하였다. 실험 결과, 대화 데이터셋을 이용한 모델 학습 시 적대적 학습 기법을 적용할 경우 다수 클래스의 분류 성능은 유지하면서 동시에 소수 클래스의 분류 성능을 크게 향상시킬 수 있음을 확인하였다.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.445-452
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• 2022

Class distribution of unbalanced data is an important part of the digital world and is a significant part of cybersecurity. Abnormal activity of unbalanced data should be found and problems solved. Although a system capable of tracking patterns in all transactions is needed, machine learning with disproportionate data, which typically has abnormal patterns, can ignore and degrade performance for minority layers, and predictive models can be inaccurately biased. In this paper, we predict target variables and improve accuracy by combining estimates using Synthetic Minority Oversampling Technique (SMOTE) and Light GBM algorithms as an approach to address unbalanced datasets. Experimental results were compared with logistic regression, decision tree, KNN, Random Forest, and XGBoost algorithms. The performance was similar in accuracy and reproduction rate, but in precision, two algorithms performed at Random Forest 80.76% and Light GBM 97.16%, and in F1-score, Random Forest 84.67% and Light GBM 91.96%. As a result of this experiment, it was confirmed that Light GBM's performance was similar without deviation or improved by up to 16% compared to five algorithms.

• Information Systems Review
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• v.26 no.2
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• pp.169-183
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• 2024

This study summarizes improvement strategies for addressing the imbalance problem in observed default data that must be considered when constructing a default model and compares and analyzes the performance improvement effects using data resampling techniques and default threshold adjustments. Empirical analysis results indicate that as the level of imbalance resolution in the data increases, and as the default threshold of the model decreases, the recall of the model improves. Conversely, it was found that as the level of imbalance resolution in the data decreases, and as the default threshold of the model increases, the precision of the model improves. Additionally, focusing solely on either recall or precision when addressing the imbalance problem results in a phenomenon where the other performance evaluation metrics decrease significantly due to the trade-off relationship. This study differs from most previous research by focusing on the relationship between improvement strategies for the imbalance problem of default data and the enhancement of default model performance. Moreover, it is confirmed that to enhance the practical usability of the default model, different improvement strategies for the imbalance problem should be applied depending on the main purpose of the model, and there is a need to utilize the F_β Score as a performance evaluation metric.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.7
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• pp.311-316
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• 2019

Classification problem is to predict the class to which an input data belongs. One of the most popular methods to do this is training a machine learning algorithm using the given dataset. In this case, the dataset should have a well-balanced class distribution for the best performance. However, when the dataset has an imbalanced class distribution, its classification performance could be very poor. To overcome this problem, we propose an over-sampling scheme that balances the number of data by using Conditional Generative Adversarial Networks (CGAN). CGAN is a generative model developed from Generative Adversarial Networks (GAN), which can learn data characteristics and generate data that is similar to real data. Therefore, CGAN can generate data of a class which has a small number of data so that the problem induced by imbalanced class distribution can be mitigated, and classification performance can be improved. Experiments using actual collected data show that the over-sampling technique using CGAN is effective and that it is superior to existing over-sampling techniques.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.357-371
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• 2014

There are many studies related to imbalanced data in which the class distribution is highly skewed. To address the problem of imbalanced data, previous studies deal with resampling techniques which correct the skewness of the class distribution in each sampled subset by using under-sampling, over-sampling or hybrid-sampling such as SMOTE. Ensemble methods have also alleviated the problem of class imbalanced data. In this paper, we compare around a dozen algorithms that combine the ensemble methods and resampling techniques based on simulated data sets generated by the Backbone model, which can handle the imbalance rate. The results on various real imbalanced data sets are also presented to compare the effectiveness of algorithms. As a result, we highly recommend the resampling technique combining ensemble methods for imbalanced data in which the proportion of the minority class is less than 10%. We also find that each ensemble method has a well-matched sampling technique. The algorithms which combine bagging or random forest ensembles with random undersampling tend to perform well; however, the boosting ensemble appears to perform better with over-sampling. All ensemble methods combined with SMOTE outperform in most situations.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.179-190
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• 2022

Data imbalance refers to a phenomenon in which the number of data in one category is too large or too small compared to another category. Due to this, it has been raised as a major factor that deteriorates performance in machine learning that utilizes classification algorithms. In order to solve the data imbalance problem, various ovrsampling methods for amplifying prime number distribution data have been proposed. Among them, SMOTE is the most representative method. In order to maximize the amplification effect of minority distribution data, various methods have emerged that remove noise included in data (SMOTE-IPF) or enhance only border lines (Borderline SMOTE). This paper proposes a method to ultimately improve classification performance by improving the processing method for anomaly data in the traditional SMOTE method that amplifies minority classification data. The proposed method consistently presented relatively high classification performance compared to the existing methods through experiments.

• Information Systems Review
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• v.24 no.4
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• pp.97-118
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• 2022

As generative adversarial network (GAN) based oversampling techniques have achieved impressive results in class imbalance of unstructured dataset such as image, many studies have begun to apply it to solving the problem of imbalance in structured dataset. However, these studies have failed to reflect the characteristics of structured data due to changing the data structure into an unstructured data format. In order to overcome the limitation, this study adapted CycleGAN to reflect the characteristics of structured data, and proposed hybridization of synthetic minority oversampling technique (SMOTE) and the adapted CycleGAN. In particular, this study tried to overcome the limitations of existing studies by using a one-dimensional convolutional neural network unlike previous studies that used two-dimensional convolutional neural network. Oversampling based on the method proposed have been experimented using various datasets and compared the performance of the method with existing oversampling methods such as SMOTE and adaptive synthetic sampling (ADASYN). The results indicated the proposed hybrid oversampling method showed superior performance compared to the existing methods when data have more dimensions or higher degree of imbalance. This study implied that the classification performance of oversampling structured data can be improved using the proposed hybrid oversampling method that considers the characteristic of structured data.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.304-306
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• 2001

많은 실세계의 문제에서 일반적인 패턴 분류 알고리즘들은 데이터의 불균형 문제에 어려움을 겪는다. 각각의 학습 예제에 균등한 중요도를 부여하는 기존의 기법들은 문제의 특징을 제대로 파악하지 못하는 경우가 많다. 본 논문에서는 불균형 데이터 문제를 해결하기 위해 퍼셉트론에 기반한 부스팅 기법을 제안한다. 부스팅 기법은 학습을 어렵게 하는 데이터에 집중하여 앙상블 머신을 구축하는 기법이다. 부스팅 기법에서는 약학습기를 필요로 하는데 기존 퍼셉트론의 경우 문제에 따라 약학습기(weak learner)의 조건을 만족시키지 못하는 경우가 있을 수 있다. 이에 커널을 도입한 커널 퍼셉트론을 사용하여 학습기의 표현 능력을 높였다. Reuters-21578 문서 집합을 대상으로 한 문서 여과 문제에서 부스팅 기법은 다층신경망이나 나이브 베이스 분류기보다 우수한 성능을 보였으며, 인공 데이터 실험을 통하여 부스팅의 샘플링 경향을 분석하였다.