Graph Ensemble Boosting for Imbalanced Noisy Graph Stream Classification
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Chengqi Zhang | Jia Wu | Shirui Pan | Xingquan Zhu | Shirui Pan | Jia Wu | Xingquan Zhu | Chengqi Zhang
[1] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[2] Phillipp Kaestner,et al. Linear And Nonlinear Programming , 2016 .
[3] Jure Leskovec,et al. Linear Programming Boosting for Uneven Datasets , 2003, ICML.
[4] Hong Cheng,et al. Graph classification: a diversified discriminative feature selection approach , 2012, CIKM.
[5] John N. Tsitsiklis,et al. Introduction to linear optimization , 1997, Athena scientific optimization and computation series.
[6] Zhi-Hua Zhou,et al. Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).
[7] Geoff Hulten,et al. Mining high-speed data streams , 2000, KDD '00.
[8] Chengqi Zhang,et al. Multi-Graph Learning with Positive and Unlabeled Bags , 2014, SDM.
[9] Haibo He,et al. Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach , 2011, Evol. Syst..
[10] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[11] Philip S. Yu,et al. Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.
[12] Hong Cheng,et al. Identifying bug signatures using discriminative graph mining , 2009, ISSTA.
[13] Shirui Pan,et al. CGStream: continuous correlated graph query for data streams , 2012, CIKM '12.
[14] Gregory Ditzler,et al. Incremental Learning of Concept Drift from Streaming Imbalanced Data , 2013, IEEE Transactions on Knowledge and Data Engineering.
[15] Chengqi Zhang,et al. Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams , 2012, 2012 IEEE 12th International Conference on Data Mining.
[16] Ambuj K. Singh,et al. GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases , 2009, 2009 IEEE 25th International Conference on Data Engineering.
[17] Xiaodong Lin,et al. Active Learning From Stream Data Using Optimal Weight Classifier Ensemble , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[18] A. Bifet,et al. Early Drift Detection Method , 2005 .
[19] Hongliang Fei,et al. Boosting with structure information in the functional space: an application to graph classification , 2010, KDD.
[20] Francisco Herrera,et al. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[21] Shirui Pan,et al. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Graph Classification with Imbalanced Class Distributions and Noise ∗ , 2022 .
[22] Kaspar Riesen,et al. Graph Classification by Means of Lipschitz Embedding , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[23] Sebastian Nowozin,et al. gBoost: a mathematical programming approach to graph classification and regression , 2009, Machine Learning.
[24] D. Luenberger. Optimization by Vector Space Methods , 1968 .
[25] Jie Tang,et al. ArnetMiner: extraction and mining of academic social networks , 2008, KDD.
[26] Xin Yao,et al. A learning framework for online class imbalance learning , 2013, 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL).
[27] Charu C. Aggarwal,et al. On Classification of Graph Streams , 2011, SDM.
[28] Stephen Kwek,et al. Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.
[29] H. Kashima,et al. Kernels for graphs , 2004 .
[30] Taghi M. Khoshgoftaar,et al. Knowledge discovery from imbalanced and noisy data , 2009, Data Knowl. Eng..
[31] Jiawei Han,et al. gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[32] George Karypis,et al. Frequent substructure-based approaches for classifying chemical compounds , 2003, IEEE Transactions on Knowledge and Data Engineering.
[33] Yuji Matsumoto,et al. An Application of Boosting to Graph Classification , 2004, NIPS.
[34] Philip S. Yu,et al. Semi-supervised feature selection for graph classification , 2010, KDD.
[35] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[36] Bin Li,et al. Fast Graph Stream Classification Using Discriminative Clique Hashing , 2013, PAKDD.
[37] Philip S. Yu,et al. Graph stream classification using labeled and unlabeled graphs , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).
[38] Yunqian Ma,et al. Imbalanced Learning: Foundations, Algorithms, and Applications , 2013 .
[39] Andrew K. C. Wong,et al. Classification of Imbalanced Data: a Review , 2009, Int. J. Pattern Recognit. Artif. Intell..
[40] Nello Cristianini,et al. Controlling the Sensitivity of Support Vector Machines , 1999 .
[41] William Nick Street,et al. A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.
[42] Tatsuya Akutsu,et al. Extensions of marginalized graph kernels , 2004, ICML.
[43] Philip S. Yu,et al. A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions , 2007, SDM.