Jason Van Hulse

According to our database1, Jason Van Hulse authored at least 58 papers between 2005 and 2014.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

On csauthors.net:

Bibliography

2014
An empirical study of the classification performance of learners on imbalanced and noisy software quality data.
Inf. Sci., 2014

Incomplete-case nearest neighbor imputation in software measurement data.
Inf. Sci., 2014

2012
Threshold-based feature selection techniques for high-dimensional bioinformatics data.
Netw. Model. Anal. Health Informatics Bioinform., 2012

Evaluation of the importance of data pre-processing order when combining feature selection and data sampling.
Int. J. Bus. Intell. Data Min., 2012

A Novel Noise-Resistant Boosting Algorithm for Class-Skewed Data.
Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

2011
Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data.
IEEE Trans. Syst. Man Cybern. Part A, 2011

Evaluating the Impact of Data Quality on Sampling.
J. Inf. Knowl. Manag., 2011

Metric Selection for Software Defect Prediction.
Int. J. Softw. Eng. Knowl. Eng., 2011

An exploration of learning when data is noisy and imbalanced.
Intell. Data Anal., 2011

Comparison of approaches to alleviate problems with high-dimensional and class-imbalanced data.
Proceedings of the IEEE International Conference on Information Reuse and Integration, 2011

A comparative evaluation of feature ranking methods for high dimensional bioinformatics data.
Proceedings of the IEEE International Conference on Information Reuse and Integration, 2011

Robustness of Filter-Based Feature Ranking: A Case Study.
Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference, 2011

2010
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance.
IEEE Trans. Syst. Man Cybern. Part A, 2010

Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors.
IEEE Trans. Neural Networks, 2010

An Empirical Evaluation of Repetitive Undersampling Techniques.
Int. J. Softw. Eng. Knowl. Eng., 2010

A novel feature selection technique for highly imbalanced data.
Proceedings of the IEEE International Conference on Information Reuse and Integration, 2010

A Novel Noise Filtering Algorithm for Imbalanced Data.
Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010

Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques.
Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010

Predicting Faults in High Assurance Software.
Proceedings of the 12th IEEE High Assurance Systems Engineering Symposium, 2010

A Comparative Study of Threshold-Based Feature Selection Techniques.
Proceedings of the 2010 IEEE International Conference on Granular Computing, 2010

An Evaluation of Sampling on Filter-Based Feature Selection Methods.
Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, 2010

2009
Improving Software-Quality Predictions With Data Sampling and Boosting.
IEEE Trans. Syst. Man Cybern. Part A, 2009

Empirical Case Studies in Attribute Noise Detection.
IEEE Trans. Syst. Man Cybern. Part C, 2009

Identifying Learners Robust to Low Quality Data.
Informatica (Slovenia), 2009

Hybrid sampling for imbalanced data.
Integr. Comput. Aided Eng., 2009

Knowledge discovery from imbalanced and noisy data.
Data Knowl. Eng., 2009

Aggregating Performance Metrics for Classifier Evaluation.
Proceedings of the IEEE International Conference on Information Reuse and Integration, 2009

An Empirical Comparison of Repetitive Undersampling Techniques.
Proceedings of the IEEE International Conference on Information Reuse and Integration, 2009

A Study on the Relationships of Classifier Performance Metrics.
Proceedings of the ICTAI 2009, 2009

An Empirical Study on Wrapper-Based Feature Ranking.
Proceedings of the ICTAI 2009, 2009

Feature Selection with High-Dimensional Imbalanced Data.
Proceedings of the ICDM Workshops 2009, 2009

2008
Imputation techniques for multivariate missingness in software measurement data.
Softw. Qual. J., 2008

A comprehensive empirical evaluation of missing value imputation in noisy software measurement data.
J. Syst. Softw., 2008

Identifying learners robust to low quality data.
Proceedings of the IEEE International Conference on Information Reuse and Integration, 2008

Improving Learner Performance with Data Sampling and Boosting.
Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2008), 2008

Resampling or Reweighting: A Comparison of Boosting Implementations.
Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2008), 2008

RUSBoost: Improving classification performance when training data is skewed.
Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008

A Comparative Study of Data Sampling and Cost Sensitive Learning.
Proceedings of the Workshops Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

Building Useful Models from Imbalanced Data with Sampling and Boosting.
Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference, 2008

Software quality modeling: The impact of class noise on the random forest classifier.
Proceedings of the IEEE Congress on Evolutionary Computation, 2008

2007
The pairwise attribute noise detection algorithm.
Knowl. Inf. Syst., 2007

The multiple imputation quantitative noise corrector.
Intell. Data Anal., 2007

Learning from Software Quality Data with Class Imbalance and Noise.
Proceedings of the Nineteenth International Conference on Software Engineering & Knowledge Engineering (SEKE'2007), 2007

Mining Data with Rare Events: A Case Study.
Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), 2007

An Empirical Study of Learning from Imbalanced Data Using Random Forest.
Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), 2007

Learning with limited minority class data.
Proceedings of the Sixth International Conference on Machine Learning and Applications, 2007

Experimental perspectives on learning from imbalanced data.
Proceedings of the Machine Learning, 2007

Skewed Class Distributions and Mislabeled Examples.
Proceedings of the Workshops Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

2006
Determining noisy instances relative to attributes of interest.
Intell. Data Anal., 2006

Class noise detection using frequent itemsets.
Intell. Data Anal., 2006

Polishing Noise in Continuous Software Measurement Data.
Proceedings of the Eighteenth International Conference on Software Engineering & Knowledge Engineering (SEKE'2006), 2006

Multiple Imputation of Software Measurement Data: A Case Study.
Proceedings of the Eighteenth International Conference on Software Engineering & Knowledge Engineering (SEKE'2006), 2006

Software quality imputation in the presence of noisy data.
Proceedings of the 2006 IEEE International Conference on Information Reuse and Integration, 2006

Noise correction using bayesian multiple imputation.
Proceedings of the 2006 IEEE International Conference on Information Reuse and Integration, 2006

A Hybrid Approach to Cleansing Software Measurement Data.
Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2006), 2006

A Comparison of Software Fault Imputation Procedures.
Proceedings of the Fifth International Conference on Machine Learning and Applications, 2006

2005
Identifying noisy features with the Pairwise Attribute Noise Detection Algorithm.
Intell. Data Anal., 2005

Identifying noise in an attribute of interest.
Proceedings of the Fourth International Conference on Machine Learning and Applications, 2005


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