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Nitesh V. Chawla: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Tanu Malik, Randal C. Burns, Nitesh V. Chawla
    A Black-Box Approach to Query Cardinality Estimation. [Citation Graph (0, 0)][DBLP]
    CIDR, 2007, pp:56-67 [Conf]
  2. Nitesh V. Chawla, Kevin W. Bowyer
    Random Subspaces and Subsampling for 2-D Face Recognition. [Citation Graph (0, 0)][DBLP]
    CVPR (2), 2005, pp:582-589 [Conf]
  3. Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer
    Bagging Is a Small-Data-Set Phenomenon. [Citation Graph (0, 0)][DBLP]
    CVPR (2), 2001, pp:684-689 [Conf]
  4. Nitesh V. Chawla, Steven Eschrich, Lawrence O. Hall
    Creating Ensembles of Classifiers. [Citation Graph (0, 0)][DBLP]
    ICDM, 2001, pp:580-581 [Conf]
  5. 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. [Citation Graph (0, 0)][DBLP]
    BIOKDD, 2001, pp:50-59 [Conf]
  6. Steven Eschrich, Nitesh V. Chawla, Lawrence O. Hall
    Generalization Methods in Bioinformatics. [Citation Graph (0, 0)][DBLP]
    BIOKDD, 2002, pp:25-32 [Conf]
  7. Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer
    Learning Rules from Distributed Data. [Citation Graph (0, 0)][DBLP]
    Large-Scale Parallel Data Mining, 1999, pp:211-220 [Conf]
  8. Nitesh V. Chawla, Kevin W. Bowyer
    Designing Multiple Classifier Systems for Face Recognition. [Citation Graph (0, 0)][DBLP]
    Multiple Classifier Systems, 2005, pp:407-416 [Conf]
  9. Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer
    Distributed Pasting of Small Votes. [Citation Graph (0, 0)][DBLP]
    Multiple Classifier Systems, 2002, pp:52-61 [Conf]
  10. Nitesh V. Chawla
    Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees. [Citation Graph (0, 0)][DBLP]
    MLCW, 2005, pp:41-55 [Conf]
  11. Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, Kevin W. Bowyer
    SMOTEBoost: Improving Prediction of the Minority Class in Boosting. [Citation Graph (0, 0)][DBLP]
    PKDD, 2003, pp:107-119 [Conf]
  12. Alec Pawling, Nitesh V. Chawla, Amitabh Chaudhary
    Evaluation of Summarization Schemes for Learning in Streams. [Citation Graph (0, 0)][DBLP]
    PKDD, 2006, pp:347-358 [Conf]
  13. 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. [Citation Graph (0, 0)][DBLP]
    SC, 2006, pp:102- [Conf]
  14. Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer
    SMOTE: Synthetic Minority Over-sampling Technique. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2002, v:16, n:, pp:321-357 [Journal]
  15. Predrag Radivojac, Nitesh V. Chawla, A. Keith Dunker, Zoran Obradovic
    Classification and knowledge discovery in protein databases. [Citation Graph (0, 0)][DBLP]
    Journal of Biomedical Informatics, 2004, v:37, n:4, pp:224-239 [Journal]
  16. Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
    Learning Ensembles from Bites: A Scalable and Accurate Approach. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:421-451 [Journal]
  17. Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Clayton Springer
    Distributed learning with bagging-like performance. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2003, v:24, n:1-3, pp:455-471 [Journal]
  18. Nitesh V. Chawla, Nathalie Japkowicz, Aleksander Kotcz
    Editorial: special issue on learning from imbalanced data sets. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2004, v:6, n:1, pp:1-6 [Journal]
  19. 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. [Citation Graph (0, 0)][DBLP]
    Computer Speech & Language, 2006, v:20, n:4, pp:468-494 [Journal]
  20. Nitesh V. Chawla, Kevin W. Bowyer
    Actively Exploring Creation of Face Space(s) for Improved Face Recognition. [Citation Graph (0, 0)][DBLP]
    AAAI, 2007, pp:809-814 [Conf]
  21. Gregory R. Madey, Albert-László Barabási, Nitesh V. Chawla, Marta Gonzalez, David Hachen, Brett Lantz, Alec Pawling, Timothy Schoenharl, Gábor Szabó, Pu Wang, Ping Yan
    Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management. [Citation Graph (0, 0)][DBLP]
    International Conference on Computational Science (1), 2007, pp:1090-1097 [Conf]
  22. Nitesh V. Chawla, Jared Sylvester
    Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets. [Citation Graph (0, 0)][DBLP]
    MCS, 2007, pp:397-406 [Conf]
  23. Nitesh V. Chawla, Grigoris J. Karakoulas
    Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2005, v:23, n:, pp:331-366 [Journal]

  24. Predicting individual disease risk based on medical history. [Citation Graph (, )][DBLP]


  25. Troubleshooting thousands of jobs on production grids using data mining techniques. [Citation Graph (, )][DBLP]


  26. Troubleshooting Distributed Systems via Data Mining. [Citation Graph (, )][DBLP]


  27. Detecting Fractures in Classifier Performance. [Citation Graph (, )][DBLP]


  28. Start Globally, Optimize Locally, Predict Globally: Improving Performance on Imbalanced Data. [Citation Graph (, )][DBLP]


  29. Scaling up Classifiers to Cloud Computers. [Citation Graph (, )][DBLP]


  30. Applying Learning Algorithms to Music Generation. [Citation Graph (, )][DBLP]


  31. Evolutionary Ensemble Creation and Thinning. [Citation Graph (, )][DBLP]


  32. Data mining on the grid for the grid. [Citation Graph (, )][DBLP]


  33. An exploration of climate data using complex networks. [Citation Graph (, )][DBLP]


  34. Mining in a mobile environment. [Citation Graph (, )][DBLP]


  35. New perspectives and methods in link prediction. [Citation Graph (, )][DBLP]


  36. Analyzing PETs on Imbalanced Datasets When Training and Testing Class Distributions Differ. [Citation Graph (, )][DBLP]


  37. Privacy-Preserving Network Aggregation. [Citation Graph (, )][DBLP]


  38. Generating Diverse Ensembles to Counter the Problem of Class Imbalance. [Citation Graph (, )][DBLP]


  39. Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams. [Citation Graph (, )][DBLP]


  40. Learning Decision Trees for Unbalanced Data. [Citation Graph (, )][DBLP]


  41. A Robust Decision Tree Algorithm for Imbalanced Data Sets. [Citation Graph (, )][DBLP]


  42. Authentication anomaly detection: a case study on a virtual private network. [Citation Graph (, )][DBLP]


  43. Modeling a Store's Product Space as a Social Network. [Citation Graph (, )][DBLP]


  44. User Generated Content Consumption and Social Networking in Knowledge-Sharing OSNs. [Citation Graph (, )][DBLP]


  45. Automatically countering imbalance and its empirical relationship to cost. [Citation Graph (, )][DBLP]


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