计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 129-134.doi: 10.11896/jsjkx.210300180
董奇达1, 王喆1, 吴松洋2
DONG Qi-da1, WANG Zhe1, WU Song-yang2
摘要: 不平衡问题在现实世界中普遍存在,而不平衡数据的分布不平衡性会严重影响模型的性能。不平衡数据通常从两方面影响模型性能:一方面是数量上的不平衡导致多数类的数据对参数有更多的更新,导致模型更加偏向多数类;另一方面是少数类样本特别少,多样性不足从而导致模型表征能力不足。针对上述问题,提出了一个结合注意力机制与几何信息的特征融合框架。具体而言,该模型首先通过预训练使模型学习数据的语义信息和判别性信息,并结合注意力机制发掘模型对不同类别数据的关注点。在第二阶段,模型通过几何信息挖掘边界特征,并且结合第一阶段得到的注意力权重对边界特征进行融合,从而对少数类的数据进行补充。基于长尾CIFAR10,CIFAR100和KDDCup99数据集的实验结果表明,所提的结合注意力机制与几何信息的特征融合框架能够有效提升对不平衡数据的分类性能,并且对于不同类型的数据,包括图像数据和结构化数据,都能有效提高分类性能。
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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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