Computer Science ›› 2022, Vol. 49 ›› Issue (5): 129-134.doi: 10.11896/jsjkx.210300180
• Database & Big Data & Data Science • Previous Articles Next Articles
DONG Qi-da1, WANG Zhe1, WU Song-yang2
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[1]FAYEK H M,LECH M,CAVEDON L.Evaluating deep lear-ning architectures for Speech Emotion Recognition[J].Neural Networks,2017,92(2):60-68. [2]HE T,ZHANG Z,ZHANG H,et al.Bag of tricks for image classification with convolutional neural networks[C]//Procee-dings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.New York:IEEE,2019:558-567. [3]LIPPI M,MONTEMURRO M A,ESPOSTI D M,et al.Natural Language Statistical Features of LSTM-Generated Texts[J].IEEE Transactions Neural Networks and Learning Systems,2019,30(11):3326-3337. [4]WANG Z,CAO C,ZHU Y.Entropy and Confidence-Based Undersampling Boosting Random Forests for Imbalanced Problems[J].IEEE Transactions on Neural Networks and Learning Systems,2020,31(12):5178-5191. [5]ESTABROOKS A,JO T,JAPKOWICZ N.A multiple resampling method for learning from imbalanced data sets[J].Computational Intelligence,2004,20(1):18-36. [6]LING C X,SHENG V S.Cost-sensitive learning and the class imbalance problem[J].Encyclopedia of Machine Learning,2008,2011:231-235. [7]WANG S,MINKU L L,YAO X.Resampling-based ensemblemethods for online class imbalance learning[J].IEEE Transactions on Knowledge and Data Engineering,2014,27(5):1356-1368. [8]ZHU T,LIN Y,LIU Y.Synthetic minority oversampling technique for multiclass imbalance problems[J].Pattern Recognition,2017,72:327-340. [9]FANG L,AU O C,TANG K,et al.Antialiasing filter design for subpixel downsampling via frequency-domain analysis[J].IEEE Transactions Image Processing,2012,21(3):1391-1405. [10]CHAWLA N V,BOWYER K W,HALL L O,et al.SMOTE:synthetic minority over-sampling technique[J].Journal of Artificial Intelligence Research,2002,16:321-357. [11]HAN H,WANG W Y,MAO B H.Borderline-SMOTE:A New Over-Sampling Method in Imbalanced Data Sets Learning[C]//International Conference on Intelligent Computing.Berlin:Springer,2005:878-887. [12]ZADROZNY B,LANGFORD J,ABE N.Cost-sensitive learning by cost-proportionate example weighting[C]//Third IEEE International Conference on Data Mining.New York:IEEE,2003:435-442. [13]KHAN S H,HAYAT M,BENNAMOUN M,et al.Cost-sensitive learning of deep feature representations from imbalanced data[J].IEEE Transactions on Neural Networks and Learning Systems,2017,29(8):3573-3587. [14]CHAWLA N V,LAZAREVIC A,HALL L O,et al.SMOTEBoost:Improving prediction of the minority class in boosting[C]//European Conference on Principles of Data Mining and Knowledge Discovery.Berlin:Springer,2003:107-119. [15]SEIFFERT C,KHOSHGOFTAAR T M,VAN HULSE J,et al.RUSBoost:A hybrid approach to alleviating class imbalance[J].IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,2009,40(1):185-197. [16]FAN W,STOLFO S J,ZHANG J,et al.AdaCost:misclassification cost-sensitive boosting[C]//16th International Conference on Machine Learning.New York:ACM,1999:97-105. [17]FREUND Y,SCHAPIRE R E.A decision-theoretic generalization of on-line learning and an application to boosting[J].Journal of Computer and System Sciences,1997,55(1):119-139. [18]YE H J,CHEN H Y,ZHAN D C,et al.Identifying and compensating for feature deviation in imbalanced deep learning[J].ar-Xiv:2001.01385,2020. [19]DONG Q,GONG S,ZHU X.Imbalanced deep learning by minority class incremental rectification[J].IEEE Transactions on Pattern analysis and Machine Intelligence,2018,41(6):1367-1381. [20]ZHOU B,CUI Q,WEI X S,et al.Bbn:Bilateral-branch network with cumulative learning for long-tailed visual recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.New York:IEEE,2020:9719-9728. [21]KANG B,XIE S,ROHRBACH M,et al.Decoupling representation and classifier for long-tailed recognition[J].arXiv:1910.09217,2019. [22]CUI Y,JIA M,LIN T Y,et al.Class-balanced loss based on effective number of samples[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.New York:IEEE,2019:9268-9277. [23]JAMAL M A,BROWN M,YANG M H,et al.Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.New York:IEEE,2020:7610-7619. [24]ZHOU P,ZHOU Z P,WANG L,et al.Intrusion detection me-thod based on autoencoder and ResNet[J].Application Research of Computers,2020,37(S2):224-226. [25]ZHANG H,CISSE M,DAUPHIN Y N,et al.mixup:Beyondempirical risk minimization[J].arXiv:1710.09412,2017. [26]CHOU H P,CHANG S C,PAN J Y,et al.Remix:Rebalanced Mixup[C]//European Conference on Computer Vision.Berlin:Springer,2020:95-110. [27]WANG Y X,GIRSHICK R,HEBERT M,et al.Low-shot lear-ning from imaginary data[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.New York:IEEE,2018:7278-7286. [28]ZOU Y,YU Z,KUMAR B V K,et al.Unsupervised domainadaptation for semantic segmentation via class-balanced self-training[C]//Proceedings of the European Conference on Computer Vision.Berlin:Springer,2018:289-305. [29]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial networks[J].arXiv:1406.2661,2014. [30]MARIANI G,SCHEIDEGGER F,ISTRATE R,et al.Bagan:Data augmentation with balancing gan[J].arXiv:1803.09655,2018. [31]ZHOU F,YANG S,FUJITA H,et al.Deep learning fault diagnosis method based on global optimization GAN for unbalanced data[J].Knowledge-Based Systems,2020,187:104837. [32]LI C,XU T,ZHU J,et al.Triple generative adversarial nets[C]//Advances in Neural Information Processing Systems.Massachusetts:MIT Press,2017:4088-4098. [33]PUJOL O,MASIP D.Geometry-based ensembles:toward astructural characterization of the classification boundary[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(6):1140-1146. [34]ZHU Z,WANG Z,LI D,et al.Geometric structural ensemble learning for imbalanced problems[J].IEEE Transactions on Cybernetics,2018,50(4):1617-1629. [35]TORRES L C B,CASTRO C L,COELHO F,et al.Large Margin Gaussian Mixture Classifier With a Gabriel Graph Geometric Representation of Data Set Structure[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(3):1400-1406. [36]GHASEMIGOL M,MONSEFI R,YAZDI H S.Ellipse support vector data description[C]//International Conference on Engineering Applications of Neural Networks.Berlin:Springer, 2009:257-268. [37]ZHU Y,WANG Z,GAO D.Gravitational fixed radius nearestneighbor for imbalanced problem[J].Knowledge-Based Systems,2015,90:224-238. [38]LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision.New York:IEEE,2017:2980-2988. [39]VERMA V,LAMB A,BECKHAM C,et al.Manifold mixup:Better representations by interpolating hidden states[C]//International Conference on Machine Learning.New York:ACM,2019:6438-6447. [40]CAO C D,WEI C L,GAIDON A,et al.Learning imbalanced datasets with label distribution-aware margin loss[C]//Advances in Neural Information Processing Systems.Massachusetts:MIT Press,2019:1-18. [41]SHRIVASTAVA A,GUPTA A,GIRSHICK R.Training re-gion-based object detectors with online hard example mining[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE,2016:761-769. |
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