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 |