데이터 불균형 해소를 위한 유전알고리즘 기반 최적의 오버샘플링 비율
Optimal Ratio of Data Oversampling Based on a Genetic Algorithm for Overcoming Data Imbalance
1
조회
0
다운로드
4
인용
0
참고문헌
키워드
초록
최근에는 데이터베이스의 발달로 금융, 보안, 네트워크 등에서 생성된 많은 데이터가 저장 가능하며, 기계학습 기반 분류기를 통해 분석이 이루어지고 있다. 이 때 주로 야기되는 문제는 데이터 불균형으로, 학습 시 다수 범주의 데이터들로 과적합이 되어 분류 정확도가 떨어지는 경우가 발생한다. 이를 해결하기 위해 소수 범주의 데이터 수를 증가시키는 오버샘플링 전략이 주로 사용되며, 데이터 분포에 적합한 기법과 인자들을 다양하게 조절하는 과정이 필요하다. 이러한 과정의 개선을 위해 본 연구에서는 스모트와 생성적 적대 신경망 등 다양한 기법 기반의 오버샘플링 조합과 비율을 유전알고리즘을 통해 탐색하고 최적화 하는 전략을 제안한다. 제안된 전략과 단일 오버샘플링 기법으로 신용카드 사기 탐지 데이터를 샘플링 한 뒤, 각각의 데이터들로 학습한 분류기의 성능을 비교한다. 그 결과 유전알고리즘으로 기법별 비율을 탐색하여 최적화 한 전략의 성능이 기존 전략들 보다 우수했다.
Abstract
Recently, with the development of database, it is possible to store a lot of data generated in finance, security, and networks. These data are being analyzed through classifiers based on machine learning. The main problem at this time is data imbalance. When we train imbalanced data, it may happen that classification accuracy is degraded due to over-fitting with majority class data. To overcome the problem of data imbalance, oversampling strategy that increases the quantity of data of minority class data is widely used. It requires to tuning process about suitable method and parameters for data distribution. To improve the process, In this study, we propose a strategy to explore and optimize oversampling combinations and ratio based on various methods such as synthetic minority oversampling technique and generative adversarial networks through genetic algorithms. After sampling credit card fraud detection which is a representative case of data imbalance, with the proposed strategy and single oversampling strategies, we compare the performance of trained classifiers with each data. As a result, a strategy that is optimized by exploring for ratio of each method with genetic algorithms was superior to previous strategies.
참고문헌 (16)
- V. Chandola, "Anomaly detection : a survey", ACM Computing Surveys, 41(3), pp.1~58 (2009)
- Monowar H. Bhuyan, "Network Anomaly Detection: Methods, Systems and Tools", IEEE Communications Surveys & Tutorials, 16(1), pp.303~336 (2014), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Nathalie Japkowicz, "The class imbalance problem: A systematic study1", Intelligent Data Analysis, 6(5), pp.429~449 (2002), IOS Press[DOI]
- H. Y. Cho, "A genetic algorithm to optimize SMOTE and GAN ratio in class imbalanced datasets", Proceedings of the Genetic and Evolutionary Computation Conference, pp.33~34 (2020)
- H. Y. Cho., "Optimization of Data Oversampling Ratio Using a Genetic Algorithm", Master, pp.2020
- N. V. Chawla, "SMOTE: Synthetic Minority Over-sampling Technique", Journal of Artificial Intelligence Research, 16, pp.321~357 (2002), AI Access Foundation[DOI]
- H. Han, "Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning", International Conference on Intelligent Computing, pp.878~887 (2005), Springer
- J. Mathew, "Kernel-based SMOTE for SVM classification of imbalanced datasets", Annual Conference of the IEEE Industrial Electronics Society, pp.1127~1132 (2015)
- Georgios Douzas, "Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE", Information Sciences, 465, pp.1~20 (2018), Elsevier BV[DOI]
- H. He, "ADASYN:adaptive synthetic sampling approach for imbalanced learning", IEEE International Joint Conference on Neural Networks, pp.1322~1328 (2008)
- I. J. Goodfellow, "Generative adversarial nets", Annual Conference on Neural Information Processing Systems, pp.2672~2680 (2014), Curran Associates
- Georgios Douzas, "Effective data generation for imbalanced learning using conditional generative adversarial networks", Expert Systems with Applications, 91, pp.464~471 (2018), Elsevier BV[DOI]
- J. H. Holland, "Adaptions in Natural and Artificial Systems : An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence" (1992), MIT Press
- Chang Wook Ahn, "Elitism-based compact genetic algorithms", IEEE Transactions on Evolutionary Computation, 7(4), pp.367~385 (2003), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Jürgen Schmidhuber, "Deep learning in neural networks: An overview", Neural Networks, 61, pp.85~117 (2015), Elsevier BV[DOI]
- D. M. Powers., "Evaluation : from precision, recall and F-measure to ROC, informedness, markedness and correlation", Journal of Machine Learning Technologies, 2(1), pp.37~63 (2011)