Nitesh V. Chawla

2009
38	EE	David A. Cieslak, Nitesh V. Chawla: A framework for monitoring classifiers' performance: when and why failure occurs? Knowl. Inf. Syst. 18(1): 83-108 (2009)
2008
37	EE	Darcy A. Davis, Nitesh V. Chawla, Nicholas Blumm, Nicholas Christakis, Albert-László Barabási: Predicting individual disease risk based on medical history. CIKM 2008: 769-778
36	EE	David A. Cieslak, Nitesh V. Chawla: Learning Decision Trees for Unbalanced Data. ECML/PKDD (1) 2008: 241-256
35	EE	David A. Cieslak, Nitesh V. Chawla, Douglas Thain: Troubleshooting thousands of jobs on production grids using data mining techniques. GRID 2008: 217-224
34	EE	David A. Cieslak, Nitesh V. Chawla: Start Globally, Optimize Locally, Predict Globally: Improving Performance on Imbalanced Data. ICDM 2008: 143-152
33	EE	Christopher Moretti, Karsten Steinhaeuser, Douglas Thain, Nitesh V. Chawla: Scaling up Classifiers to Cloud Computers. ICDM 2008: 472-481
32	EE	Nitesh V. Chawla, Douglas Thain, Ryan Lichtenwalter, David A. Cieslak: Data mining on the grid for the grid. IPDPS 2008: 1-5
31	EE	David A. Cieslak, Nitesh V. Chawla: Analyzing PETs on Imbalanced Datasets When Training and Testing Class Distributions Differ. PAKDD 2008: 519-526
30	EE	Nitesh V. Chawla, David A. Cieslak, Lawrence O. Hall, Ajay Joshi: Automatically countering imbalance and its empirical relationship to cost. Data Min. Knowl. Discov. 17(2): 225-252 (2008)
29	EE	Qi Liao, David A. Cieslak, Aaron Striegel, Nitesh V. Chawla: Using selective, short-term memory to improve resilience against DDoS exhaustion attacks. Security and Communication Networks 1(4): 287-299 (2008)
2007
28		Nitesh V. Chawla, Kevin W. Bowyer: Actively Exploring Creation of Face Space(s) for Improved Face Recognition. AAAI 2007: 809-814
27	EE	Tanu Malik, Randal C. Burns, Nitesh V. Chawla: A Black-Box Approach to Query Cardinality Estimation. CIDR 2007: 56-67
26	EE	David A. Cieslak, Nitesh V. Chawla: Detecting Fractures in Classifier Performance. ICDM 2007: 123-132
25	EE	Gregory R. Madey, Albert-László Barabási, Nitesh V. Chawla, Marta Gonzalez, David Hachen, Brett Lantz, Alec Pawling, Timothy W. Schoenharl, Gábor Szabó, Pu Wang, Ping Yan: Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management. International Conference on Computational Science (1) 2007: 1090-1097
24	EE	Nitesh V. Chawla, Jared Sylvester: Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets. MCS 2007: 397-406
23	EE	Michael J. Chapple, Nitesh V. Chawla, Aaron Striegel: Authentication anomaly detection: a case study on a virtual private network. MineNet 2007: 17-22
2006
22	EE	David A. Cieslak, Douglas Thain, Nitesh V. Chawla: Troubleshooting Distributed Systems via Data Mining. HPDC 2006: 309-312
21	EE	Jared Sylvester, Nitesh V. Chawla: Evolutionary Ensemble Creation and Thinning. IJCNN 2006: 5148-5155
20	EE	Alec Pawling, Nitesh V. Chawla, Amitabh Chaudhary: Evaluation of Summarization Schemes for Learning in Streams. PKDD 2006: 347-358
19	EE	Tanu Malik, Randal C. Burns, Nitesh V. Chawla, Alexander S. Szalay: Data management and query - Estimating query result sizes for proxy caching in scientific database federations. SC 2006: 102
18	EE	Yang Liu, Nitesh V. Chawla, Mary P. Harper, Elizabeth Shriberg, Andreas Stolcke: A study in machine learning from imbalanced data for sentence boundary detection in speech. Computer Speech & Language 20(4): 468-494 (2006)
2005
17	EE	Nitesh V. Chawla, Kevin W. Bowyer: Random Subspaces and Subsampling for 2-D Face Recognition. CVPR (2) 2005: 582-589
16	EE	Nitesh V. Chawla: Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees. MLCW 2005: 41-55
15	EE	Nitesh V. Chawla, Kevin W. Bowyer: Designing Multiple Classifier Systems for Face Recognition. Multiple Classifier Systems 2005: 407-416
14		Nitesh V. Chawla: Data Mining for Imbalanced Datasets: An Overview. The Data Mining and Knowledge Discovery Handbook 2005: 853-867
13	EE	Nitesh V. Chawla, Grigoris J. Karakoulas: Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains. J. Artif. Intell. Res. (JAIR) 23: 331-366 (2005)
2004
12	EE	Predrag Radivojac, Nitesh V. Chawla, A. Keith Dunker, Zoran Obradovic: Classification and knowledge discovery in protein databases. Journal of Biomedical Informatics 37(4): 224-239 (2004)
11	EE	Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer: Learning Ensembles from Bites: A Scalable and Accurate Approach. Journal of Machine Learning Research 5: 421-451 (2004)
10	EE	Nitesh V. Chawla, Nathalie Japkowicz, Aleksander Kotcz: Editorial: special issue on learning from imbalanced data sets. SIGKDD Explorations 6(1): 1-6 (2004)
2003
9	EE	Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, Kevin W. Bowyer: SMOTEBoost: Improving Prediction of the Minority Class in Boosting. PKDD 2003: 107-119
8	EE	Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Clayton Springer: Distributed learning with bagging-like performance. Pattern Recognition Letters 24(1-3): 455-471 (2003)
2002
7	EE	Steven Eschrich, Nitesh V. Chawla, Lawrence O. Hall: Generalization Methods in Bioinformatics. BIOKDD 2002: 25-32
6	EE	Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer: Distributed Pasting of Small Votes. Multiple Classifier Systems 2002: 52-61
5	EE	Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer: SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. (JAIR) 16: 321-357 (2002)
2001
4	EE	Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer: Investigation of bagging-like effects and decision trees versus neural nets in protein secondary structure prediction. BIOKDD 2001: 50-59
3	EE	Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer: Bagging Is a Small-Data-Set Phenomenon. CVPR (2) 2001: 684-689
2	EE	Nitesh V. Chawla, Steven Eschrich, Lawrence O. Hall: Creating Ensembles of Classifiers. ICDM 2001: 580-581
1999
1	EE	Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer: Learning Rules from Distributed Data. Large-Scale Parallel Data Mining 1999: 211-220

Coauthor Index

1	Albert-László Barabási	[25] [37]
2	Nicholas Blumm	[37]
3	Kevin W. Bowyer	[1] [3] [4] [5] [6] [8] [9] [11] [15] [17] [28]
4	Randal C. Burns	[19] [27]
5	Michael J. Chapple	[23]
6	Amitabh Chaudhary	[20]
7	Nicholas Christakis	[37]
8	David A. Cieslak	[22] [26] [29] [30] [31] [32] [34] [35] [36] [38]
9	Darcy A. Davis	[37]
10	A. Keith Dunker	[12]
11	Steven Eschrich	[2] [7]
12	Marta Gonzalez	[25]
13	David Hachen	[25]
14	Lawrence O. Hall	[1] [2] [3] [4] [5] [6] [7] [8] [9] [11] [30]
15	Mary P. Harper	[18]
16	Nathalie Japkowicz	[10]
17	Ajay Joshi	[30]
18	Grigoris J. Karakoulas	[13]
19	W. Philip Kegelmeyer	[1] [3] [4] [5] [6] [8] [11]
20	Aleksander Kotcz	[10]
21	Brett Lantz	[25]
22	Aleksandar Lazarevic	[9]
23	Qi Liao	[29]
24	Ryan Lichtenwalter	[32]
25	Yang Liu	[18]
26	Gregory R. Madey	[25]
27	Tanu Malik	[19] [27]
28	Thomas E. Moore	[3] [4] [6] [8]
29	Christopher Moretti	[33]
30	Zoran Obradovic	[12]
31	Alec Pawling	[20] [25]
32	Predrag Radivojac	[12]
33	Timothy W. Schoenharl	[25]
34	Elizabeth Shriberg	[18]
35	Clayton Springer	[3] [4] [8]
36	Karsten Steinhaeuser	[33]
37	Andreas Stolcke	[18]
38	Aaron Striegel	[23] [29]
39	Jared Sylvester	[21] [24]
40	Gábor Szabó	[25]
41	Alexander S. Szalay	[19]
42	Douglas Thain	[22] [32] [33] [35]
43	Pu Wang	[25]
44	Ping Yan	[25]

Colors in the list of coauthors