Jason Van Hulse - researchr alias

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Viewing Publication 1 - 63 from 63

2014

Incomplete-case nearest neighbor imputation in software measurement dataJason Van Hulse, Taghi M. Khoshgoftaar. isci, 259:596-610, 2014. [doi]

An empirical study of the classification performance of learners on imbalanced and noisy software quality dataChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Andres Folleco. isci, 259:571-595, 2014. [doi]

2012

2011

Robustness of Filter-Based Feature Ranking: A Case StudyWilker Altidor, Taghi M. Khoshgoftaar, Jason Van Hulse. flairs 2011: [doi]

A comparative evaluation of feature ranking methods for high dimensional bioinformatics dataJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano. iri 2011: 315-320 [doi]

Evaluating the Impact of Data Quality on SamplingJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano. jikm, 10(3):225-245, 2011. [doi]

An exploration of learning when data is noisy and imbalancedJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano. ida, 15(2):215-236, 2011. [doi]

Comparing Boosting and Bagging Techniques With Noisy and Imbalanced DataTaghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. tsmc, 41(3):552-568, 2011. [doi]

Comparison of approaches to alleviate problems with high-dimensional and class-imbalanced dataAhmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse. iri 2011: 234-239 [doi]

Metric Selection for Software Defect PredictionHuanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse, Kehan Gao. ijseke, 21(2):237-257, 2011. [doi]

2010

Comparative Analysis of DNA Microarray Data through the Use of Feature Selection TechniquesDavid J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse. icmla 2010: 147-152 [doi]

An Evaluation of Sampling on Filter-Based Feature Selection MethodsKehan Gao, Taghi M. Khoshgoftaar, Jason Van Hulse. flairs 2010: [doi]

A Novel Noise Filtering Algorithm for Imbalanced DataJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano. icmla 2010: 9-14 [doi]

Evaluating the impact of data quality on samplingJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano. iri 2010: 31-36 [doi]

An Empirical Evaluation of Repetitive Undersampling TechniquesJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano. ijseke, 20(2):173-195, 2010. [doi]

A novel feature selection technique for highly imbalanced dataTaghi M. Khoshgoftaar, Kehan Gao, Jason Van Hulse. iri 2010: 80-85 [doi]

Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errorsTaghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. tnn, 21(5):813-830, 2010. [doi]

RUSBoost: A Hybrid Approach to Alleviating Class ImbalanceChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. tsmc, 40(1):185-197, 2010. [doi]

Predicting Faults in High Assurance SoftwareNaeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse. hase 2010: 26-34 [doi]

A Comparative Study of Threshold-Based Feature Selection TechniquesHuanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse. grc 2010: 499-504 [doi]

2009

An Empirical Study on Wrapper-Based Feature RankingWilker Altidor, Taghi M. Khoshgoftaar, Jason Van Hulse. ictai 2009: 75-82 [doi]

Identifying Learners Robust to Low Quality DataAndres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. informaticaSI, 33(3):245-259, 2009. [doi]

An Empirical Comparison of Repetitive Undersampling TechniquesJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano. iri 2009: 29-34 [doi]

Feature Selection with High-Dimensional Imbalanced DataJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano, Randall Wald. icdm 2009: 507-514 [doi]

Knowledge discovery from imbalanced and noisy dataJason Van Hulse, Taghi M. Khoshgoftaar. DKE, 68(12):1513-1542, 2009. [doi]

Empirical Case Studies in Attribute Noise DetectionTaghi M. Khoshgoftaar, Jason Van Hulse. tsmc, 39(4):379-388, 2009. [doi]

Improving Software-Quality Predictions With Data Sampling and BoostingChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse. tsmc, 39(6):1283-1294, 2009. [doi]

Hybrid sampling for imbalanced dataChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse. icae, 16(3):193-210, 2009. [doi]

Aggregating Performance Metrics for Classifier EvaluationNaeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse. iri 2009: 35-40 [doi]

A Study on the Relationships of Classifier Performance MetricsNaeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse. ictai 2009: 59-66 [doi]

2008

Software quality modeling: The impact of class noise on the random forest classifierAndres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Lofton A. Bullard. cec 2008: 3853-3859 [doi]

Identifying learners robust to low quality dataAndres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Lofton A. Bullard. iri 2008: 190-195 [doi]

A comprehensive empirical evaluation of missing value imputation in noisy software measurement dataJason Van Hulse, Taghi M. Khoshgoftaar. jss, 81(5):691-708, 2008. [doi]

Imputation techniques for multivariate missingness in software measurement dataTaghi M. Khoshgoftaar, Jason Van Hulse. sqj, 16(4):563-600, 2008. [doi]

Hybrid sampling for imbalanced dataChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse. iri 2008: 202-207 [doi]

A Comparative Study of Data Sampling and Cost Sensitive LearningChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. icdm 2008: 46-52 [doi]

RUSBoost: Improving classification performance when training data is skewedChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. icpr 2008: 1-4 [doi]

Resampling or Reweighting: A Comparison of Boosting ImplementationsChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. ictai 2008: 445-451 [doi]

Building Useful Models from Imbalanced Data with Sampling and BoostingChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. flairs 2008: 306-311

Improving Learner Performance with Data Sampling and BoostingChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. ictai 2008: 452-459 [doi]

2007

Learning from Software Quality Data with Class Imbalance and NoiseAndres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert. seke 2007: 487

Skewed Class Distributions and Mislabeled ExamplesJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano. icdm 2007: 477-482 [doi]

Incomplete-Case Nearest Neighbor Imputation in Software Measurement DataJason Van Hulse, Taghi M. Khoshgoftaar. iri 2007: 630-637 [doi]

Experimental perspectives on learning from imbalanced dataJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano. icml 2007: 935-942 [doi]

The pairwise attribute noise detection algorithmJason Van Hulse, Taghi M. Khoshgoftaar, Haiying Huang. kais, 11(2):171-190, 2007. [doi]

Learning with limited minority class dataTaghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse, Amri Napolitano, Andres Folleco. icmla 2008: 348-353 [doi]

An Empirical Study of Learning from Imbalanced Data Using Random ForestTaghi M. Khoshgoftaar, Moiz Golawala, Jason Van Hulse. ictai 2007: 310-317 [doi]

The multiple imputation quantitative noise correctorTaghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert, Lili Zhao. ida, 11(3):245-263, 2007. [doi]

An Empirical Study of the Classification Performance of Learners on Imbalanced and Noisy Software Quality DataChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Andres Folleco. iri 2007: 651-658 [doi]

Mining Data with Rare Events: A Case StudyChris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano. ictai 2007: 132-139 [doi]

2006

A Comparison of Software Fault Imputation ProceduresJason Van Hulse, Taghi M. Khoshgoftaar, Chris Seiffert. icmla 2006: 135-142 [doi]

Class noise detection using frequent itemsetsJason Van Hulse, Taghi M. Khoshgoftaar. ida, 10(6):487-507, 2006. [doi]

Noise correction using bayesian multiple imputationJason Van Hulse, Taghi M. Khoshgoftaar, Chris Seiffert, Lili Zhao. iri 2006: 478-483

Multiple Imputation of Software Measurement Data: A Case StudyTaghi M. Khoshgoftaar, Jason Van Hulse. seke 2006: 220-226

A Hybrid Approach to Cleansing Software Measurement DataTaghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert. ictai 2006: 713-722 [doi]

Polishing Noise in Continuous Software Measurement DataTaghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse. seke 2006: 227-231

Software quality imputation in the presence of noisy dataTaghi M. Khoshgoftaar, Andres Folleco, Jason Van Hulse, Lofton A. Bullard. iri 2006: 484-489

Determining noisy instances relative to attributes of interestTaghi M. Khoshgoftaar, Jason Van Hulse. ida, 10(3):251-268, 2006. [doi]

2005

Empirical case studies in attribute noise detectionTaghi M. Khoshgoftaar, Jason Van Hulse. iri 2005: 211-216 [doi]

Identifying noise in an attribute of interestTaghi M. Khoshgoftaar, Jason Van Hulse. icmla 2005: [doi]

Identifying noisy features with the Pairwise Attribute Noise Detection AlgorithmTaghi M. Khoshgoftaar, Jason Van Hulse. ida, 9(6):589-602, 2005. [doi]

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2005

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