An imbalanced data learning method for tool breakage detection based on generative adversarial networks

2021 ◽  
Author(s):  
Shixu Sun ◽  
Xiaofeng Hu ◽  
Yingchao Liu
Keyword(s):  
Imbalanced Data ◽  
Generative Adversarial Networks ◽  
Learning Method ◽  
Tool Breakage ◽  
Adversarial Networks ◽  
Imbalanced Data Learning

A model-based reinforcement learning method based on conditional generative adversarial networks

2021 ◽  
Vol 152 ◽  
pp. 18-25
Author(s):  
Tingting Zhao ◽  
Ying Wang ◽  
Guixi Li ◽  
Le Kong ◽  
Yarui Chen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Generative Adversarial Networks ◽  
Learning Method ◽  
Model Based ◽  
Adversarial Networks

scholarly journals Handling Imbalanced Data in Intrusion Detection Systems using Generative Adversarial Networks

2020 ◽  
Vol 2020 (1) ◽  
pp. 1-13
Author(s):  
Ly Vu ◽  
Quang Uy Nguyen
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Imbalanced Data ◽  
Original Data ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Detection Systems ◽  
Adversarial Networks ◽  
Attack Data

Machine learning-based intrusion detection hasbecome more popular in the research community thanks to itscapability in discovering unknown attacks. To develop a gooddetection model for an intrusion detection system (IDS) usingmachine learning, a great number of attack and normal datasamples are required in the learning process. While normaldata can be relatively easy to collect, attack data is muchrarer and harder to gather. Subsequently, IDS datasets areoften dominated by normal data and machine learning modelstrained on those imbalanced datasets are ineffective in detect-ing attacks. In this paper, we propose a novel solution to thisproblem by using generative adversarial networks to generatesynthesized attack data for IDS. The synthesized attacks aremerged with the original data to form the augmented dataset.Three popular machine learning techniques are trained on theaugmented dataset. The experiments conducted on the threecommon IDS datasets and one our own dataset show thatmachine learning algorithms achieve better performance whentrained on the augmented dataset of the generative adversarialnetworks compared to those trained on the original datasetand other sampling techniques. The visualization techniquewas also used to analyze the properties of the synthesizeddata of the generative adversarial networks and the others.

Enhanced generative adversarial networks for fault diagnosis of rotating machinery with imbalanced data

2019 ◽  
Vol 30 (11) ◽  
pp. 115005 ◽  
Author(s):  
Qi Li ◽  
Liang Chen ◽  
Changqing Shen ◽  
Bingru Yang ◽  
Zhongkui Zhu
Keyword(s):  
Fault Diagnosis ◽  
Imbalanced Data ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Generative inter-class transformations for imbalanced data weather classification

2021 ◽  
Vol 2021 (1) ◽  
pp. 16-20
Author(s):  
Apostolia Tsirikoglou ◽  
Marcus Gladh ◽  
Daniel Sahlin ◽  
Gabriel Eilertsen ◽  
Jonas Unger
Keyword(s):  
Data Augmentation ◽  
Imbalanced Data ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Medical Applications ◽  
Single Image ◽  
Adversarial Networks ◽  
Augmentation Strategies ◽  
Future Work ◽  
Small Dataset

This paper presents an evaluation of how data augmentation and inter-class transformations can be used to synthesize training data in low-data scenarios for single-image weather classification. In such scenarios, augmentations is a critical component, but there is a limit to how much improvements can be gained using classical augmentation strategies. Generative adversarial networks (GAN) have been demonstrated to generate impressive results, and have also been successful as a tool for data augmentation, but mostly for images of limited diversity, such as in medical applications. We investigate the possibilities in using generative augmentations for balancing a small weather classification dataset, where one class has a reduced number of images. We compare intra-class augmentations by means of classical transformations as well as noise-to-image GANs, to interclass augmentations where images from another class are transformed to the underrepresented class. The results show that it is possible to take advantage of GANs for inter-class augmentations to balance a small dataset for weather classification. This opens up for future work on GAN-based augmentations in scenarios where data is both diverse and scarce.

Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

Measurement ◽  
2020 ◽  
Vol 152 ◽  
pp. 107377 ◽  
Author(s):  
Wei Zhang ◽  
Xiang Li ◽  
Xiao-Dong Jia ◽  
Hui Ma ◽  
Zhong Luo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Imbalanced Data ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Machinery Fault Diagnosis

scholarly journals Imbalanced data learning by minority class augmentation using capsule adversarial networks

2020 ◽  
Author(s):  
Pourya Shamsolmoali ◽  
Masoumeh Zareapoor ◽  
Linlin Shen ◽  
Abdul Hamid Sadka ◽  
Jie Yang
Keyword(s):  
Imbalanced Data ◽  
Minority Class ◽  
Adversarial Networks ◽  
Imbalanced Data Learning

Research on Imbalanced Data Classification Based on Classroom-Like Generative Adversarial Networks

2022 ◽  
pp. 1-29
Author(s):  
Yancheng Lv ◽  
Lin Lin ◽  
Jie Liu ◽  
Hao Guo ◽  
Changsheng Tong
Keyword(s):  
Loss Function ◽  
Imbalanced Data ◽  
Data Classification ◽  
Generative Adversarial Networks ◽  
Complex Data ◽  
Channel Measurements ◽  
Data Set ◽  
Machine Learning Classification ◽  
Adversarial Networks ◽  
Imbalanced Data Classification

Abstract Most of the research on machine learning classification methods is based on balanced data; the research on imbalanced data classification needs improvement. Generative adversarial networks (GANs) are able to learn high-dimensional complex data distribution without relying on a prior hypothesis, which has become a hot technology in artificial intelligence. In this letter, we propose a new structure, classroom-like generative adversarial networks (CLGANs), to construct a model with multiple generators. Taking inspiration from the fact that teachers arrange teaching activities according to students' learning situation, we propose a weight allocation function to adaptively adjust the influence weight of generator loss function on discriminator loss function. All the generators work together to improve the degree of discriminator and training sample space, so that a discriminator with excellent performance is trained and applied to the tasks of imbalanced data classification. Experimental results on the Case Western Reserve University data set and 2.4 GHz Indoor Channel Measurements data set show that the data classification ability of the discriminator trained by CLGANs with multiple generators is superior to that of other imbalanced data classification models, and the optimal discriminator can be obtained by selecting the right matching scheme of the generator models.

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