Lawrence Hall

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Dmitry GoldgofDistinguished Professor of Computer Science and Engineering, University of South Florida, MoffittVerified 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
Robert GilliesChair of Cancer Physiology, H. Lee Moffitt Cancer CenterVerified email at moffitt.org
Nitesh V ChawlaFrank Freimann Professor of Computer Science & Engg., Notre DameVerified email at nd.edu
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
Rahul PaulCBER, FDAVerified email at mgh.harvard.edu
Mu ZhouStanford University; Rutgers UniversityVerified email at stanford.edu
Virendra KumarProfessor, Department of NMR, All India Institute of Medical SciencesVerified email at aiims.edu
Samuel HawkinsBradley UniversityVerified email at fsmail.bradley.edu
Andrew RemsenBureau of Ocean Energy ManagementVerified email at boem.gov
Dmitry CherezovPhDVerified email at usf.edu
Hady Ahmady PhouladyCalifornia State University, SacramentoVerified email at csus.edu
Prodip HoreDirector of Machine Learning & AI at American ExpressVerified email at aexp.com
Saeed S. AlahmariUniversity of South FloridaVerified email at usf.edu

Lawrence Hall

Professor of Computer Science and Engineering, University of South Florida

Verified email at mail.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	24227	2002
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	1962	2003
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	1906	2012
Review of MR image segmentation techniques using pattern recognition. JC Bezdek, LO Hall, LP Clarke Medical physics 20 (4), 1033-1048, 1993	1488	1993
MRI segmentation: methods and applications LP Clarke, RP Velthuizen, MA Camacho, JJ Heine, M Vaidyanathan, ... Magnetic resonance imaging 13 (3), 343-368, 1995	1074	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	812	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	703	1998
Clustering with a genetically optimized approach LO Hall, IB Ozyurt, JC Bezdek IEEE Transactions on evolutionary Computation 3 (2), 103-112, 1999	597	1999
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	575	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	573	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	543	2012
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	393	2001
Partially supervised clustering for image segmentation AM Bensaid, LO Hall, JC Bezdek, LP Clarke Pattern recognition 29 (5), 859-871, 1996	387	1996
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	318	2014
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	304	2008
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	302	2018
Ensemble diversity measures and their application to thinning RE Banfield, LO Hall, KW Bowyer, WP Kegelmeyer Information Fusion 6 (1), 49-62, 2005	301	2005
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	298	2005
MRI segmentation using fuzzy clustering techniques MC Clark, LO Hall, DB Goldgof, LP Clarke, RP Velthuizen, MS Silbiger IEEE Engineering in Medicine and Biology Magazine 13 (5), 730-742, 1994	298	1994
Fast accurate fuzzy clustering through data reduction S Eschrich, J Ke, LO Hall, DB Goldgof IEEE transactions on fuzzy systems 11 (2), 262-270, 2003	287	2003

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