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A label noise filtering and label missing supplement framework based on game theory

Digital Communications and Networks 2023年第4期9卷 887-895页

作者： Yuwen Liu Rongju Yao Song Jia Fan Wang Ruili Wang Rui Ma Lianyong Qi College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Weifang Key Laboratory of Blockchain on Agricultural Vegetables Weifang University of Science and TechnologyShouguangChina China Unicom Taian Branch China School of Natural and Computational Sciences Massey UniversityNew Zealand General Education Department Shandong First Medical UniversityShandong Academy of Medical SciencesTai'an271000China College of Computer Science and Technology Zhejiang UniversityHangzhouChina

labeled data is widely used in various classification ***,there is a huge challenge that labels are often added *** labels added by malicious users will affect the training effect of the *** unreliability of labeled data has hindered the *** order to solve the above problems,we propose a framework of label noise Filtering and Missing label Supplement(LNFS).And we take location labels in Location-Based Social Networks(LBSN)as an example to implement our *** the problem of label noise filtering,we first use FastText to transform the restaurant's labels into vectors,and then based on the assumption that the label most similar to all other labels in the location is most *** use cosine similarity to judge and select the most representative *** the problem of label missing,we use simple common word similarity to judge the similarity of users'comments,and then use the label of the similar restaurant to supplement the missing *** optimize the performance of the model,we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the ***,a case study is given to illustrate the effectiveness and reliability of LNFS.

关键词： label noise FastText Cosine similarity Game theory LSTM

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Handling label noise in Air Traffic Complexity Evaluation Based on Confident Learning and XGBoost

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Transactions of Nanjing University of Aeronautics and Astronautics 2020年第6期37卷 936-946页

作者： ZHANG Minghua XIE Hua ZHANG Dongfang GE Jiaming CHEN Haiyan College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air traffic controllers(ATCos).Recently,many machine learning techniques have been used to evaluate air traffic complexity by constructing a mapping from complexity related factors to air traffic complexity ***,the low quality of complexity labels,which is named as label noise,has often been neglected and caused unsatisfactory performance in air traffic complexity *** paper aims at label noise in air traffic complexity samples,and proposes a confident learning and XGBoost-based approach to evaluate air traffic complexity under label *** confident learning process is applied to filter out noisy samples with various label probability distributions,and XGBoost is used to train a robust and high-performance air traffic complexity evaluation model on the different label noise filtered ratio *** are carried out on a real dataset from the Guangzhou airspace sector in China,and the results prove that the appropriate label noise removal strategy and XGBoost algorithm can effectively mitigate the label noise problem and achieve better performance in air traffic complexity evaluation.

关键词： air traffic complexity evaluation label noise confident learning XGBoost

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label-noise robust classification with multi-view learning

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Science China(Technological Sciences) 2023年第6期66卷 1841-1854页

作者： LIANG NaiYao YANG ZuYuan LI LingJiang LI ZhenNi XIE ShengLi Guangdong Key Laboratory of IoT Information Technology School of AutomationGuangdong University of TechnologyGuangzhou 510006China Key Laboratory of iDetection and Manufacturing-IoT Ministry of EducationGuangzhou 510006China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China

label noise is often contained in the training data due to various human factors or measurement errors,which significantly causes a negative effect on *** many previous methods that have been proposed to learn robust classifiers,they are mainly based on the single-view *** the other hand,although existing multi-view classification methods benefit from the more comprehensive information,they rarely consider label *** this paper,we propose a novel label-noise robust classification model with multi-view learning to overcome these *** the proposed model,not only the classifier learning but also the label-noise removal can benefit from the multi-view ***,we relax the label matrix of the basic multi-view least squares regression model,and develop a nonlinear transformation with a natural probabilistic approximation in the process of labels,which is conveniently optimized and beneficial to improve the discriminative ability of ***,we preserve the intrinsic manifold structure of multi-view data on the relaxed label matrix,facilitating the process of label *** optimizing the proposed model with the nonlinear transformation,we derive a lemma about the partial derivation of the softmax related function,and develop an efficient alternating *** evaluations on six real-world datasets confirm the advantages of the proposed method,compared to the related state-of-the-art methods.

关键词： label noise multi-view learning classification robust least squares regression label relaxation

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A review of addressing class noise problems of remote sensing classification

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Journal of Systems Engineering and Electronics 2023年第1期34卷 36-46页

作者： FENG Wei LONG Yijun WANG Shuo QUAN Yinghui School of Electronic Engineering Xidian UniversityXi’an 710071China Xi’an Key Laboratory of Advanced Remote Sensing Xi’an 710071China Key Laboratory of Collaborative Intelligence Systems Ministry of EducationXidian UniversityXi’an 710071China

The development of image classification is one of the most important research topics in remote sensing. The prediction accuracy depends not only on the appropriate choice of the machine learning method but also on the quality of the training datasets. However, real-world data is not perfect and often suffers from noise. This paper gives an overview of noise filtering methods. Firstly, the types of noise and the consequences of class noise on machine learning are presented. Secondly, class noise handling methods at both the data level and the algorithm level are introduced. Then ensemble-based class noise handling methods including class noise removal, correction, and noise robust ensemble learners are presented. Finally, a summary of existing data-cleaning techniques is given.

关键词： class noise label noise mislabeled classification ensemble learning remote sensing

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A robust optimization method for label noisy datasets based on adaptive threshold: Adaptive-k

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Frontiers of Computer Science 2024年第4期18卷 49-60页

作者： Enes DEDEOGLU Himmet Toprak KESGIN Mehmet Fatih AMASYALI Department of Computer Engineering Yildiz Technical UniversityIstanbul 34220Turkey

The use of all samples in the optimization process does not produce robust results in datasets with label *** the gradients calculated according to the losses of the noisy samples cause the optimization process to go in the wrong *** this paper,we recommend using samples with loss less than a threshold determined during the optimization,instead of using all samples in the *** proposed method,Adaptive-k,aims to exclude label noise samples from the optimization process and make the process *** noisy datasets,we found that using a threshold-based approach,such as Adaptive-k,produces better results than using all samples or a fixed number of low-loss samples in the *** the basis of our theoretical analysis and experimental results,we show that the Adaptive-k method is closest to the performance of the Oracle,in which noisy samples are entirely removed from the ***-k is a simple but effective *** does not require prior knowledge of the noise ratio of the dataset,does not require additional model training,and does not increase training time *** the experiments,we also show that Adaptive-k is compatible with different optimizers such as SGD,SGDM,and *** code for Adaptive-k is available at GitHub.

关键词： robust optimization label noise noisy label deep learning noisy datasets noise ratio estimation robust training

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Learning from crowds with robust support vector machines

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Science China(Information Sciences) 2023年第3期66卷 133-149页

作者： Wenjun YANG Chaoqun LI Liangxiao JIANG School of Mathematics and Physics China University of Geosciences School of Computer Science China University of Geosciences

Crowdsourcing system provides an easy way to obtain labeled training data. However, the labels provided by non-expert labelers often appear low quality. So in practice, each sample usually obtains a multiple label set from multiple different labelers. Learning-from-crowds(LFC) aims to design ground truth inference algorithms to infer the unknown true labels of data from multiple label sets. Despite their proper statistical foundations, the existing ground truth inference algorithms show limited performance when the number of labelers is small. However, more labelers mean higher costs. This paper tries to propose a novel ground truth inference algorithm which can maintain moderate performance and simultaneously reduce labeling costs. This paper addresses LFC from a point of view of robust classifiers and presents a new label noise robust support vector machine inference(RSVMI) algorithm. We prove that only one convex quadratic programming problem needs to be solved to build a robust support vector machine. Furthermore,in order to apply the robust support vector machine to crowdsourced data, two methods are proposed to estimate the noise level of integrated labels. By transforming the original LFC problem into a robust classifier learning problem, our algorithm shows good performance when the number of labelers is very small. In our experiments, the minimum number of labelers is set to 3. In terms of both label quality and model quality,the experimental results on benchmark data sets and real-world data sets show the effectiveness of RSVMI.

关键词： crowdsourcing learning ground truth inference integrated labels label noise robust support vector machines

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