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Publications of Author

1. Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert
   A Hybrid Approach to Cleansing Software Measurement Data. [Citation Graph (0, 0)][DBLP]
   ICTAI, 2006, pp:713-722 [Conf]
   
2. Jason Van Hulse, Taghi M. Khoshgoftaar, Chris Seiffert, Lili Zhao
   Noise correction using bayesian multiple imputation. [Citation Graph (0, 0)][DBLP]
   IRI, 2006, pp:478-483 [Conf]
   
3. Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliya
   Labeling network event records for intrusion detection in a Wireless LAN. [Citation Graph (0, 0)][DBLP]
   IRI, 2006, pp:200-206 [Conf]
   
4. Taghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse
   Polishing Noise in Continuous Software Measurement Data. [Citation Graph (0, 0)][DBLP]
   SEKE, 2006, pp:227-231 [Conf]
   
5. Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliya
   Low-Effort Labeling of Network Events for Intrusion Detection in WLANs. [Citation Graph (0, 0)][DBLP]
   FLAIRS Conference, 2007, pp:490-495 [Conf]
   
6. Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Andres Folleco
   An Empirical Study of the Classification Performance of Learners on Imbalanced and Noisy Software Quality Data. [Citation Graph (0, 0)][DBLP]
   IRI, 2007, pp:651-658 [Conf]
   
7. Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert
   Learning from Software Quality Data with Class Imbalance and Noise. [Citation Graph (0, 0)][DBLP]
   SEKE, 2007, pp:487-0 [Conf]
   
8. Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert, Lili Zhao
   The multiple imputation quantitative noise corrector. [Citation Graph (0, 0)][DBLP]
   Intell. Data Anal., 2007, v:11, n:3, pp:245-263 [Journal]
   
9. 
   Building Useful Models from Imbalanced Data with Sampling and Boosting. [Citation Graph (, )][DBLP]
   
   
10. 
    A Comparative Study of Data Sampling and Cost Sensitive Learning. [Citation Graph (, )][DBLP]
    
    
11. 
    Learning with limited minority class data. [Citation Graph (, )][DBLP]
    
    
12. 
    A Comparison of Software Fault Imputation Procedures. [Citation Graph (, )][DBLP]
    
    
13. 
    RUSBoost: Improving classification performance when training data is skewed. [Citation Graph (, )][DBLP]
    
    
14. 
    Resampling or Reweighting: A Comparison of Boosting Implementations. [Citation Graph (, )][DBLP]
    
    
15. 
    Improving Learner Performance with Data Sampling and Boosting. [Citation Graph (, )][DBLP]
    
    
16. 
    Mining Data with Rare Events: A Case Study. [Citation Graph (, )][DBLP]
    
    
17. 
    Hybrid sampling for imbalanced data. [Citation Graph (, )][DBLP]