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8th International Conference on big data and Information Analytics, bigDIA 2022

作者： Wang, Ke Luo, Menghua Deng, Mingsen Chen, Haiwen Information College Guizhou University of Finance and Economics Guiyang China Big Data Application and Economics Colllege Guizhou University of Finance and Economics Guiyang China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

In the field of data science, the imputation of missing data is a common and important problem. Accompanied by the development of artificial intelligence, the data analysis methods of machine learning have received unprecedented attention. As one kind of the important algorithms of machine learning, Random Forest has been sought after by researchers for its excellent practical effect and reasonable interpretation. In this paper, a Nested Random Forest imputation method is proposed to deal with the missing data problem. To ensure the authenticity of the filling data, this method not only considers the overall distribution of the data but also pays attention to the personalized characteristics of the data. From the experimental results, the Nested Random Forest algorithm achieves better effects than the seven mainstream baseline methods in three real-world datasets of different application fields. © 2022 IEEE.

关键词： Machine learning

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Generative Adversarial Minority Enlargement–A Local Linear Over-Sampling Synthetic Method

SSRN

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SSRN 2022年

作者： Wang, Ke Zhou, Tongqing Luo, Menghua Li, Xionglue Cai, Zhiping Computer Colllege National University of Defense Technology Changsha China Information Colllege GuiZhou University of Finance and Economics Guiyang China Big Data Application and Economics Colllege GuiZhou University of Finance and Economics Guiyang China

The imbalanced data problem is widely recognized in real-world datasets. To avoid learning bias on imbalanced data, the over-sampling strategy is well studied and adopted for generating synthetic minority samples. Wherein, the Synthetic Minority Over-sampling Technology and its improved algorithms become standard baselines. In recent years, the popular Generative Adversarial Networks and its enhanced variants, introduced from the computer vision community, are believed to generate better samples, by approximating the true data distribution. Yet, we notice that the synthetic data for the minority category in this line of designs is usually restrained in a limited feature space, which may mislead the classifiers trained on them to take data in the unsampled region of the minority category as from the majority category. Given such limitations, we propose a Generative Adversarial Minority Enlargement (GAME) method to intentionally extend the sampling margin during data generative and adversarial phases. This is accomplished by adjusting the parameters of a local linear model to approach the majority category. We conduct an extensive evaluation on 28 datasets of different domains, extracted from the UCI real-world datasets. The results show that GAME can achieve more balanced and stable results efficiently than 12 state-of-the-art methods. © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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