| Deep learning applications and challenges in big data analytics MM Najafabadi, F Villanustre, TM Khoshgoftaar, N Seliya, R Wald, ... Journal of big data 2, 1-21, 2015 | 2769 | 2015 |
| A survey on addressing high-class imbalance in big data JL Leevy, TM Khoshgoftaar, RA Bauder, N Seliya Journal of Big Data 5 (1), 1-30, 2018 | 690 | 2018 |
| Choosing software metrics for defect prediction: an investigation on feature selection techniques K Gao, TM Khoshgoftaar, H Wang, N Seliya Software: Practice and Experience 41 (5), 579-606, 2011 | 361 | 2011 |
| A study on the relationships of classifier performance metrics N Seliya, TM Khoshgoftaar, J Van Hulse 2009 21st IEEE international conference on tools with artificial …, 2009 | 301 | 2009 |
| Comparative assessment of software quality classification techniques: An empirical case study TM Khoshgoftaar, N Seliya Empirical Software Engineering 9, 229-257, 2004 | 228 | 2004 |
| Analyzing software measurement data with clustering techniques S Zhong, TM Khoshgoftaar, N Seliya IEEE Intelligent Systems 19 (2), 20-27, 2004 | 220 | 2004 |
| Attribute selection and imbalanced data: Problems in software defect prediction TM Khoshgoftaar, K Gao, N Seliya 2010 22nd IEEE International conference on tools with artificial …, 2010 | 208 | 2010 |
| Tree-based software quality estimation models for fault prediction TM Khoshgoftaar, N Seliya Proceedings Eighth IEEE Symposium on Software Metrics, 203-214, 2002 | 202 | 2002 |
| Fault prediction modeling for software quality estimation: Comparing commonly used techniques TM Khoshgoftaar, N Seliya Empirical software engineering 8, 255-283, 2003 | 197 | 2003 |
| Evolutionary optimization of software quality modeling with multiple repositories Y Liu, TM Khoshgoftaar, N Seliya IEEE Transactions on Software Engineering 36 (6), 852-864, 2010 | 179 | 2010 |
| Clustering-based network intrusion detection S Zhong, TM Khoshgoftaar, N Seliya International Journal of reliability, Quality and safety Engineering 14 (02 …, 2007 | 173 | 2007 |
| Unsupervised learning for expert-based software quality estimation. S Zhong, TM Khoshgoftaar, N Seliya HASE, 149-155, 2004 | 171 | 2004 |
| A survey on the state of healthcare upcoding fraud analysis and detection R Bauder, TM Khoshgoftaar, N Seliya Health Services and Outcomes Research Methodology 17, 31-55, 2017 | 122 | 2017 |
| An empirical study of predicting software faults with case-based reasoning TM Khoshgoftaar, N Seliya, N Sundaresh Software Quality Journal 14, 85-111, 2006 | 116 | 2006 |
| Analogy-based practical classification rules for software quality estimation TM Khoshgoftaar, N Seliya Empirical Software Engineering 8, 325-350, 2003 | 116 | 2003 |
| A literature review on one-class classification and its potential applications in big data N Seliya, A Abdollah Zadeh, TM Khoshgoftaar Journal of Big Data 8, 1-31, 2021 | 115 | 2021 |
| Machine learning for detecting brute force attacks at the network level MM Najafabadi, TM Khoshgoftaar, C Kemp, N Seliya, R Zuech 2014 IEEE International Conference on Bioinformatics and Bioengineering, 379-385, 2014 | 114 | 2014 |
| Software quality classification modeling using the SPRINT decision tree algorithm TM Khoshgoftaar, N Seliya International Journal on Artificial Intelligence Tools 12 (03), 207-225, 2003 | 108 | 2003 |
| Software quality analysis of unlabeled program modules with semisupervised clustering N Seliya, TM Khoshgoftaar IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and …, 2007 | 107 | 2007 |
| Software quality estimation with limited fault data: a semi-supervised learning perspective N Seliya, TM Khoshgoftaar Software Quality Journal 15 (3), 327-344, 2007 | 105 | 2007 |
