ref1.bib

%mc-ccr原文
@article{2020Combined,
  title={Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise},
  author={ Koziarski, Micha  and  Woniak, Micha  and  Krawczyk, Bartosz },
  year={2020},
}

%简述不平衡数据集的论文
@book{2016A,
  title={A Survey of Predictive Modeling on Imbalanced Domains},
  author={ Branco, P.  and  Torgo, L.  and  Ribeiro, R. P. },
  publisher={ACM},
  year={2016},
}

%adaboost
@InProceedings{10.1007/3-540-59119-2_166,
author="Freund, Yoav
and Schapire, Robert E.",
editor="Vit{\'a}nyi, Paul",
title="A desicion-theoretic generalization of on-line learning and an application to boosting",
booktitle="Computational Learning Theory",
year="1995",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="23--37",
abstract="We consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update rule of Littlestone and Warmuth [10] can be adapted to this mode yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games and prediction of points in ℝn. We also show how the weight-update rule can be used to derive a new boosting algorithm which does not require prior knowledge about the performance of the weak learning algorithm.",
isbn="978-3-540-49195-8"
}

%xgboost
@article{Chen_2016,
   title={XGBoost},
   ISBN={9781450342322},
   url={http://dx.doi.org/10.1145/2939672.2939785},
   DOI={10.1145/2939672.2939785},
   journal={Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
   publisher={ACM},
   author={Chen, Tianqi and Guestrin, Carlos},
   year={2016},
   month={Aug}
}

%dbscan的方法
@article{2020Over,
  title={Over-sampling algorithm for imbalanced data classification},
  author={ Xiaolong, Xu  and  Wen, Chen  and  Yanfei, Sun },
  journal={Journal of Systems Engineering and Electronics},
  volume={30},
  number={6},
  pages={1182-1191},
  year={2020},
}

%smoteboost
@inproceedings{2003SMOTEBoost,
  title={SMOTEBoost: Improving Prediction of the Minority Class in Boosting},
  author={ Chawla, Nitesh V.  and  Lazarevic, Aleksandar  and  Hall, Lawrence O.  and  Bowyer, Kevin W. },
  booktitle={European Conference on Principles of Data Mining and Knowledge Discovery},
  year={2003},
}


%介绍cost-sensitive
@article{article,
author = {Kotsiantis, Sotiris and Kanellopoulos, D. and Pintelas, P.},
year = {2005},
month = {11},
pages = {25-36},
title = {Handling imbalanced datasets: A review},
volume = {30},
journal = {GESTS International Transactions on Computer Science and Engineering}
}



%一个cost-sensitive的应用
@inproceedings{2007Highcost-sensitive,
  title={High Detection-rate Cascades for Real-Time Object Detection},
  author={ Masnadi-Shirazi, Hamed  and  Vasconcelos, Nuno },
  booktitle={Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
  year={2007},
}

%cost-sensitive介绍
@inproceedings{2010Risk,
  title={Risk minimization, probability elicitation, and cost-sensitive SVMs},
  author={ Masnadi-Shirazi, Hamed  and  Vasconcelos, Nuno },
  booktitle={International Conference on Machine Learning},
  year={2010},
}


%ensemble方法介绍
@article{article_boosting,
author = {Ferreira, A. and Figueiredo, M.},
year = {2012},
month = {01},
pages = {35-85},
title = {Boosting algorithms: A review of methods, theory, and applications},
volume = {3},
journal = {Ensemble Machine Learning: Methods and Applications},
doi = {10.1007/978-1-4419-9326-7_2}
}


%smote原文
@article{2002SMOTE,
  title={SMOTE: Synthetic Minority Over-sampling Technique},
  author={ Chawla, Nitesh V.  and  Bowyer, Kevin W.  and  Hall, Lawrence O.  and  Kegelmeyer, W. Philip },
  journal={Journal of Artificial Intelligence Research},
  volume={16},
  number={1},
  pages={321-357},
  year={2002},
}


%boardline 原文
@inproceedings{2005Borderline,
  title={Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning},
  author={ Han, Hui  and  Wang, Wenyuan  and  Mao, Binghuan },
  booktitle={International Conference on Intelligent Computing},
  year={2005},
}

%adasyn原文
@inproceedings{2008ADASYN,
  title={ADASYN: Adaptive synthetic sampling approach for imbalanced learning},
  author={ He, Haibo  and  Bai, Yang  and  Garcia, Edwardo A.  and  Li, Shutao },
  booktitle={Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on},
  year={2008},
}

%另一篇cost-sensitive
@article{2017Cost,
  title={Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data},
  author={ Khan, Salman H.  and  Hayat, Munawar  and  Bennamoun, Mohammed  and  Sohel, Ferdous A.  and  Togneri, Roberto },
  journal={IEEE Transactions on Neural Networks & Learning Systems},
  pages={1-15},
  year={2017},
}

%介绍bagging
@article{2018Multi,
  title={Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data},
  author={ Lango, Mateusz  and  Stefanowski, Jerzy },
  journal={Journal of Intelligent Information Systems},
  volume={50},
  number={1},
  pages={97-127},
  year={2018},
}

%引入多类别评测指标
@book{2017Relevance,
  title={Relevance-Based Evaluation Metrics for Multi-class Imbalanced Domains},
  author={ Branco, Paula  and Luís Torgo and  Ribeiro, Rita P.  and  Branco, Paula  and Luís Torgo and  Ribeiro, Rita P.  and  Branco, Paula  and Luís Torgo and  Ribeiro, Rita P.  and  Branco, Paula },
  year={2017},
}

%ccr原文
@article{2017CCR,
  title={CCR: A COMBINED CLEANING AND RESAMPLING ALGORITHM FOR IMBALANCED DATA CLASSIFICATION},
  author={ Koziarski, Michal  and  Wozniak, Michal },
  journal={International Journal of Applied Mathematics and Computer ence},
  volume={27},
  number={4},
  pages={727-736},
  year={2017},
}

%radial的多分类方法
@article{2019Radial,
  title={Radial-Based Oversampling for Multiclass Imbalanced Data Classification},
  author={ Krawczyk, Bartosz  and  Koziarski, Michal  and  Wozniak, Michal },
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  volume={PP},
  number={99},
  pages={1-14},
  year={2019},
}

%介绍聚类算法的文章
@article{2011Data,
  title={Data Mining: Practical Machine Learning Tools and Techniques, Third Edition},
  author={ Witten, I. H  and  Frank, Eibe  and  Hall, Mark A  and  Booksx, Inc },
  journal={Acm Sigmod Record},
  volume={31},
  number={1},
  pages={76-77},
  year={2011},
}

%不平衡数据
@article{2004Editorial,
  title={Editorial: Special issues on learning from imbalanced data sets},
  author={ Chawla, Nv  and  Japkowic, N  and  Kotcz, A  and  Japkowicz, N },
  journal={Annals of Nuclear Energy},
  volume={36},
  number={3},
  pages={255-257},
  year={2004},
}


%医疗检测
@article{2013Computational,
  title={Computational intelligence for heart disease diagnosis: A medical knowledge driven approach},
  author={ Nahar, Jesmin  and  Imam, Tasadduq  and  Tickle, Kevin S.  and  Chen, Yi Ping Phoebe },
  journal={Expert Systems with Applications},
  volume={40},
  number={1},
  pages={96-104},
  year={2013},
}


%overlapping
@article{Jierui2013Overlapping,
  title={Overlapping community detection in networks: The state-of-the-art and comparative study},
  author={Jierui and Xie and Stephen and Kelley and Boleslaw and K. and Szymanski},
  journal={Acm Computing Surveys},
  year={2013},
}

%医疗检测，smote_ENn
@article{2019Electrocardiogram,
  title={Electrocardiogram Diagnosis Based on SMOTE+ENN and Random Forest},
  author={ Sun, Li  and  Shang, Ziwei  and  Cao, Qing  and  Chen, Kang  and  Li, Jiyun },
  year={2019},
}

%qizhajiance
@article{2018Imbalanced,
  title={Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning},
  author={ Zhang, Yuyan  and  Li, Xinyu  and  Gao, Liang  and  Wang, Lihui  and  Wen, Long },
  journal={Journal of Manufacturing Systems},
  volume={48},
  number={Part C},
  pages={34-50},
  year={2018},
}

%pianxiangduoshulei
@article{Victoria2013An,
  title={An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics},
  author={Victoria López and Alberto Fernández and Salvador García and  Palade, Vasile  and  Herrera, Francisco },
  journal={Information ences},
  volume={250},
  pages={113-141},
  year={2013},
}


@article{Myoung2015Geometric,
  title={Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction},
  author={Myoung-Jong and Kim and Dae-Ki and Kang and Hong and Bae and Kim},
  journal={Expert Systems with Applications},
  year={2015},
}


@book{2004A,
  title={A study of the behavior of several methods for balancing machine learning training data},
  author={ Monard, Maria Carolina  and  Monard, Maria Carolina  and  Monard, Maria Carolina },
  publisher={ACM},
  year={2004},
}




%说明多分类任务要比普通任务复杂的文章
@ARTICLE{multi_class,  author={S. {Wang} and X. {Yao}},  
journal={IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},   
title={Multiclass Imbalance Problems: Analysis and Potential Solutions},   
year={2012},  volume={42},  number={4},  pages={1119-1130},  doi={10.1109/TSMCB.2012.2187280}
}