Lawrence Hall

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Dmitry GoldgofDistinguished Professor of Computer Science and Engineering, University of South FloridaVerified email at mail.usf.edu
Kevin W. BowyerSchubmehl-Prein Family Professor of Computer Science and Engineering, University of Notre DameVerified email at nd.edu
William Philip KegelmeyerSandia National LaboratoriesVerified email at sandia.gov
Nitesh V Chawla, Fellow: ACM, IEEE,...Frank Freimann Professor of CSE.,Director, Lucy Family Institute for Data & Soc, Univ. of Notre DameVerified email at nd.edu
Robert GilliesChair of Cancer Physiology, H. Lee Moffitt Cancer CenterVerified email at moffitt.org
James C. BezdekUniversity of Melbourne, Computer and Information SystemsVerified email at unimelb.edu.au
Matthew B. Schabath, Ph.D.H. Lee Moffitt Cancer Center & Research InstituteVerified email at moffitt.org
Steven EschrichMoffitt Cancer CenterVerified email at moffitt.org
Yoganand (Yoga) BalagurunathanMoffitt Cancer CenterVerified email at moffitt.org
peter moutonUniversity of South FloridaVerified email at disector.com
Kurt KramerLarcos AquacultureVerified email at larcosaqua.com
Mu ZhouStanford University; Rutgers UniversityVerified email at stanford.edu
Virendra KumarProfessor, Department of NMR, All India Institute of Medical SciencesVerified email at aiims.edu
Hady Ahmady PhouladyCalifornia State University, SacramentoVerified email at csus.edu
Dmitry CherezovPhDVerified email at usf.edu
Andrew RemsenBureau of Ocean Energy ManagementVerified email at boem.gov
Prodip HoreDirector of Machine Learning & AI at American ExpressVerified email at aexp.com
Kaoutar BEN AHMEDFlorida International UniversityVerified email at fiu.edu
Hamidreza FarhidzadehPhD in Computer Science and EngineeringVerified email at mail.usf.edu
Diane J CookWashington State UniversityVerified email at eecs.wsu.edu

Lawrence Hall

Professor of Computer Science and Engineering, University of South Florida

Verified email at usf.edu

artificial intelligence pattern recognition data mining fuzzy sets


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
SMOTE: synthetic minority over-sampling technique NV Chawla, KW Bowyer, LO Hall, WP Kegelmeyer Journal of artificial intelligence research 16, 321-357, 2002	31579	2002
Radiomics: the process and the challenges V Kumar, Y Gu, S Basu, A Berglund, SA Eschrich, MB Schabath, K Forster, ... Magnetic resonance imaging 30 (9), 1234-1248, 2012	2267	2012
SMOTEBoost: Improving prediction of the minority class in boosting NV Chawla, A Lazarevic, LO Hall, KW Bowyer Knowledge Discovery in Databases: PKDD 2003: 7th European Conference on …, 2003	2192	2003
Review of MR image segmentation techniques using pattern recognition. JC Bezdek, LO Hall, LP Clarke Medical physics 20 (4), 1033-1048, 1993	1542	1993
MRI segmentation: methods and applications LP Clarke, RP Velthuizen, MA Camacho, JJ Heine, M Vaidyanathan, ... Magnetic resonance imaging 13 (3), 343-368, 1995	1110	1995
A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain LO Hall, AM Bensaid, LP Clarke, RP Velthuizen, MS Silbiger, JC Bezdek IEEE transactions on neural networks 3 (5), 672-682, 1992	825	1992
Automatic tumor segmentation using knowledge-based techniques MC Clark, LO Hall, DB Goldgof, R Velthuizen, FR Murtagh, MS Silbiger IEEE transactions on medical imaging 17 (2), 187-201, 1998	731	1998
A comparison of decision tree ensemble creation techniques RE Banfield, LO Hall, KW Bowyer, WP Kegelmeyer IEEE transactions on pattern analysis and machine intelligence 29 (1), 173-180, 2006	629	2006
Validity-guided (re) clustering with applications to image segmentation AM Bensaid, LO Hall, JC Bezdek, LP Clarke, ML Silbiger, JA Arrington, ... IEEE Transactions on fuzzy systems 4 (2), 112-123, 1996	613	1996
Fuzzy c-means algorithms for very large data TC Havens, JC Bezdek, C Leckie, LO Hall, M Palaniswami IEEE Transactions on Fuzzy Systems 20 (6), 1130-1146, 2012	597	2012
Clustering with a genetically optimized approach LO Hall, IB Ozyurt, JC Bezdek IEEE Transactions on evolutionary Computation 3 (2), 103-112, 1999	596	1999
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images LM Fletcher-Heath, LO Hall, DB Goldgof, FR Murtagh Artificial intelligence in medicine 21 (1-3), 43-63, 2001	406	2001
Partially supervised clustering for image segmentation AM Bensaid, LO Hall, JC Bezdek, LP Clarke Pattern recognition 29 (5), 859-871, 1996	402	1996
Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches M Zhou, J Scott, B Chaudhury, L Hall, D Goldgof, KW Yeom, M Iv, Y Ou, ... American Journal of Neuroradiology 39 (2), 208-216, 2018	384	2018
Reproducibility and prognosis of quantitative features extracted from CT images Y Balagurunathan, Y Gu, H Wang, V Kumar, O Grove, S Hawkins, J Kim, ... Translational oncology 7 (1), 72-87, 2014	346	2014
Active learning to recognize multiple types of plankton. T Luo, K Kramer, DB Goldgof, LO Hall, S Samson, A Remsen, T Hopkins, ... Journal of Machine Learning Research 6 (4), 2005	337	2005
Automatically countering imbalance and its empirical relationship to cost NV Chawla, DA Cieslak, LO Hall, A Joshi Data Mining and Knowledge Discovery 17, 225-252, 2008	321	2008
Predicting malignant nodules from screening CT scans S Hawkins, H Wang, Y Liu, A Garcia, O Stringfield, H Krewer, Q Li, ... Journal of Thoracic Oncology 11 (12), 2120-2128, 2016	311	2016
Finding covid-19 from chest x-rays using deep learning on a small dataset LO Hall, R Paul, DB Goldgof, GM Goldgof arXiv preprint arXiv:2004.02060, 2020	310	2020
Ensemble diversity measures and their application to thinning RE Banfield, LO Hall, KW Bowyer, WP Kegelmeyer Information Fusion 6 (1), 49-62, 2005	310	2005

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