Nitesh V Chawla

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Kevin W. BowyerSchubmehl-Prein Family Professor of Computer Science and Engineering, University of Notre DameVerified email at nd.edu
Lawrence HallProfessor of Computer Science and Engineering, University of South FloridaVerified email at mail.usf.edu
William Philip KegelmeyerSandia National LaboratoriesVerified email at sandia.gov
Yuxiao DongCS, Tsinghua UniversityVerified email at tsinghua.edu.cn
David CieslakVP of Predictive ModelingVerified email at aunalytics.com
Chuxu ZhangAssistant Professor, Computer Science, Brandeis UniversityVerified email at brandeis.edu
Karsten SteinhaeuserComputer Science and Engineering, University of MinnesotaVerified email at umn.edu
Chao HuangAssistant Professor of Computing Science, University of Hong KongVerified email at hku.hk
Yang YangKellogg School of ManagementVerified email at kellogg.northwestern.edu
Meng JiangUniversity of Notre DameVerified email at nd.edu
Troy RaederDstilleryVerified email at dstillery.com
Auroop GangulyProfessor, Northeastern University, Boston, MAVerified email at neu.edu
Xian WuUniversity of Notre Dame, PinterestVerified email at alumni.nd.edu
Keith FeldmanChildren's Mercy Kansas CityVerified email at cmh.edu
Aaron StriegelProfessor, Department of Computer Science and Engineering, University of Notre DameVerified email at nd.edu
Jian Xu 徐健Senior Data Scientist @ Citadel | Ph.D. in Computer ScienceVerified email at jianxu.net
Reid A. JohnsonZillow GroupVerified email at nd.edu
David HachenProfessor of Sociology, University of Notre DameVerified email at nd.edu
Tang JieProfessor, Tsinghua UniversityVerified email at tsinghua.edu.cn
Louis FaustMayo ClinicVerified email at mayo.edu

Nitesh V Chawla

Frank Freimann Professor of Computer Science & Engg., Notre Dame

Verified email at nd.edu - Homepage

Data science machine learning data mining network science


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SMOTE: synthetic minority over-sampling technique NV Chawla, KW Bowyer, LO Hall, WP Kegelmeyer Journal of Artificial Intelligence Research (JAIR) 16, 321-357, 2002	18752	2002
Special issue on learning from imbalanced data sets NV Chawla, N Japkowicz, A Kotcz ACM SIGKDD explorations newsletter 6 (1), 1-6, 2004	2477	2004
SMOTEBoost: Improving prediction of the minority class in boosting NV Chawla, A Lazarevic, LO Hall, KW Bowyer European conference on principles of data mining and knowledge discovery …, 2003	1727	2003
Data mining for imbalanced datasets: An overview NV Chawla Data mining and knowledge discovery handbook, 875-886, 2009	1667	2009
metapath2vec: Scalable representation learning for heterogeneous networks Y Dong, NV Chawla, A Swami Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017	1378	2017
SVMs modeling for highly imbalanced classification Y Tang, YQ Zhang, NV Chawla, S Krasser IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39 …, 2008	951	2008
New perspectives and methods in link prediction RN Lichtenwalter, JT Lussier, NV Chawla Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010	819	2010
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary A Fernández, S Garcia, F Herrera, NV Chawla Journal of artificial intelligence research 61, 863-905, 2018	669	2018
A unifying view on dataset shift in classification JG Moreno-Torres, T Raeder, R Alaiz-Rodríguez, NV Chawla, F Herrera Pattern recognition 45 (1), 521-530, 2012	612	2012
Bringing big data to personalized healthcare: a patient-centered framework NV Chawla, DA Davis Journal of general internal medicine 28 (3), 660-665, 2013	466	2013
Heterogeneous graph neural network C Zhang, D Song, C Huang, A Swami, NV Chawla Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019	426	2019
C4. 5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure NV Chawla Proceedings of the ICML 3, 66, 2003	370	2003
Learning decision trees for unbalanced data DA Cieslak, NV Chawla Joint European Conference on Machine Learning and Knowledge Discovery in …, 2008	320	2008
Link prediction and recommendation across heterogeneous social networks Y Dong, J Tang, S Wu, J Tian, NV Chawla, J Rao, H Cao 2012 IEEE 12th International conference on data mining, 181-190, 2012	284	2012
Learning from streaming data with concept drift and imbalance: an overview TR Hoens, R Polikar, NV Chawla Progress in Artificial Intelligence 1 (1), 89-101, 2012	283	2012
Big data opportunities and challenges: Discussions from data analytics perspectives [discussion forum] ZH Zhou, NV Chawla, Y Jin, GJ Williams IEEE Computational intelligence magazine 9 (4), 62-74, 2014	281	2014
Automatically countering imbalance and its empirical relationship to cost NV Chawla, DA Cieslak, LO Hall, A Joshi Data Mining and Knowledge Discovery 17 (2), 225-252, 2008	279	2008
When will it happen? relationship prediction in heterogeneous information networks Y Sun, J Han, CC Aggarwal, NV Chawla Proceedings of the fifth ACM international conference on Web search and data …, 2012	278	2012
A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data C Zhang, D Song, Y Chen, X Feng, C Lumezanu, W Cheng, J Ni, B Zong, ... Proceedings of the AAAI conference on artificial intelligence 33 (01), 1409-1416, 2019	271	2019
Combating imbalance in network intrusion datasets. DA Cieslak, NV Chawla, A Striegel GrC, 732-737, 2006	266	2006

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