Label correlation guided borderline oversampling for imbalanced multi-label data learning

[1]  Yang Wang,et al.  Online Multi-Label Streaming Feature Selection With Label Correlation , 2023, IEEE Transactions on Knowledge and Data Engineering.

[2]  Chi-Chun Lee,et al.  Exploiting Co-occurrence Frequency of Emotions in Perceptual Evaluations To Train A Speech Emotion Classifier , 2022, INTERSPEECH.

[3]  Deyu Li,et al.  A weighted ML-KNN based on discernibility of attributes to heterogeneous sample pairs , 2022, Inf. Process. Manag..

[4]  Osmar R Zaiane,et al.  Modeling global and local label correlation with graph convolutional networks for multi-label chest X-ray image classification , 2022, Medical & Biological Engineering & Computing.

[5]  Yi Sun,et al.  Minority Sub-Region Estimation-Based Oversampling for Imbalance Learning , 2022, IEEE Transactions on Knowledge and Data Engineering.

[6]  Changzhong Zou,et al.  Label-aware graph representation learning for multi-label image classification , 2022, Neurocomputing.

[7]  Ji Zhang,et al.  Graph-based multi-label disease prediction model learning from medical data and domain knowledge , 2021, Knowl. Based Syst..

[8]  Jiucheng Xu,et al.  Feature selection using Fisher score and multilabel neighborhood rough sets for multilabel classification , 2021, Inf. Sci..

[9]  Sabrina Tiun,et al.  MULTILABEL OVER-SAMPLING AND UNDER-SAMPLING WITH CLASS ALIGNMENT FOR IMBALANCED MULTILABEL TEXT CLASSIFICATION , 2021, Journal of Information and Communication Technology.

[10]  Deyu Li,et al.  Multilabel Feature Selection Based on Relative Discernibility Pair Matrix , 2021, IEEE Transactions on Fuzzy Systems.

[11]  Mayank Vatsa,et al.  On Learning Deep Models with Imbalanced Data Distribution , 2021, AAAI.

[12]  Mario Giacobini,et al.  A review of methods for imbalanced multi-label classification , 2021, Pattern Recognit..

[13]  Junwen Bai,et al.  HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders , 2021, AAAI.

[14]  Yuhua Qian,et al.  Feature Selection With Missing Labels Using Multilabel Fuzzy Neighborhood Rough Sets and Maximum Relevance Minimum Redundancy , 2021, IEEE Transactions on Fuzzy Systems.

[15]  Qi Zhu,et al.  Addressing Class Imbalance in Federated Learning , 2020, AAAI.

[16]  Nik Bessis,et al.  Classifying emotions in Stack Overflow and JIRA using a multi-label approach , 2020, Knowl. Based Syst..

[17]  Bin Liu,et al.  Multi-Label Sampling based on Local Label Imbalance , 2020, Pattern Recognit..

[18]  Haibo Zhang,et al.  Gaussian Distribution Based Oversampling for Imbalanced Data Classification , 2020, IEEE Transactions on Knowledge and Data Engineering.

[19]  Yandre M. G. Costa,et al.  MLTL: A multi-label approach for the Tomek Link undersampling algorithm , 2020, Neurocomputing.

[20]  Yang Li,et al.  Gaussian prior based adaptive synthetic sampling with non-linear sample space for imbalanced learning , 2020, Knowl. Based Syst..

[21]  Bin Liu,et al.  Dealing with class imbalance in classifier chains via random undersampling , 2020, Knowl. Based Syst..

[22]  Michael K. Ng,et al.  Oversampling for Imbalanced Data via Optimal Transport , 2019, AAAI.

[23]  Xiu-Shen Wei,et al.  Multi-Label Image Recognition With Graph Convolutional Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Chih-Fong Tsai,et al.  Under-sampling class imbalanced datasets by combining clustering analysis and instance selection , 2019, Inf. Sci..

[25]  Shu-Ching Chen,et al.  A Multi-label Multimodal Deep Learning Framework for Imbalanced Data Classification , 2019, 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[26]  Runliang Dou,et al.  Local positive and negative correlation-based k-labelsets for multi-label classification , 2018, Neurocomputing.

[27]  Grigorios Tsoumakas,et al.  Making Classifier Chains Resilient to Class Imbalance , 2018, ACML.

[28]  Min-Ling Zhang,et al.  Feature-Induced Labeling Information Enrichment for Multi-Label Learning , 2018, AAAI.

[29]  Francisco Charte,et al.  Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets , 2017, Neurocomputing.

[30]  Dimitris N. Metaxas,et al.  Addressing Imbalance in Multi-Label Classification Using Structured Hellinger Forests , 2017, AAAI.

[31]  Francisco Charte,et al.  On the Impact of Dataset Complexity and Sampling Strategy in Multilabel Classifiers Performance , 2016, HAIS.

[32]  Francisco Charte,et al.  MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation , 2015, Knowl. Based Syst..

[33]  Francisco Charte,et al.  Addressing imbalance in multilabel classification: Measures and random resampling algorithms , 2015, Neurocomputing.

[34]  Min-Ling Zhang,et al.  Towards Class-Imbalance Aware Multi-Label Learning , 2015, IEEE Transactions on Cybernetics.

[35]  Francesca Mangili,et al.  Should We Really Use Post-Hoc Tests Based on Mean-Ranks? , 2015, J. Mach. Learn. Res..

[36]  Francisco Charte,et al.  MLeNN: A First Approach to Heuristic Multilabel Undersampling , 2014, IDEAL.

[37]  Min-Ling Zhang,et al.  A Review on Multi-Label Learning Algorithms , 2014, IEEE Transactions on Knowledge and Data Engineering.

[38]  K. Murase,et al.  MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning , 2014, IEEE Transactions on Knowledge and Data Engineering.

[39]  Josef Kittler,et al.  Inverse random under sampling for class imbalance problem and its application to multi-label classification , 2012, Pattern Recognit..

[40]  Grigorios Tsoumakas,et al.  On the Stratification of Multi-label Data , 2011, ECML/PKDD.

[41]  Grigorios Tsoumakas,et al.  MULAN: A Java Library for Multi-Label Learning , 2011, J. Mach. Learn. Res..

[42]  Geoff Holmes,et al.  Classifier chains for multi-label classification , 2009, Machine Learning.

[43]  Eyke Hüllermeier,et al.  Multilabel classification via calibrated label ranking , 2008, Machine Learning.

[44]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[45]  Eyke Hüllermeier,et al.  A Unified Model for Multilabel Classification and Ranking , 2006, ECAI.

[46]  Hui Han,et al.  Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.

[47]  Jiebo Luo,et al.  Learning multi-label scene classification , 2004, Pattern Recognit..

[48]  Lothar Thiele,et al.  A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

[49]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[50]  Sam Kwong,et al.  Active k-labelsets ensemble for multi-label classification , 2021, Pattern Recognit..

[51]  Yonghe Liu,et al.  Improving interpolation-based oversampling for imbalanced data learning , 2020, Knowl. Based Syst..

[52]  Bassam Al-Salemi,et al.  Multi-label Arabic text categorization: A benchmark and baseline comparison of multi-label learning algorithms , 2019, Inf. Process. Manag..

[53]  Xuegang Hu,et al.  Learning common and label-specific features for multi-Label classification with correlation information , 2022, Pattern Recognit..