Professor Lawrence O. Hall
hall at csee dot usf dot edu
Ph.D. in 1986 in Computer Science from Florida State University.
My research interests lie in distributed machine learning,
extreme data mining, bioinformatics, pattern
recognition and integrating AI into image processing. The
exploitation of imprecision with the use of fuzzy logic in pattern
recognition, AI and learning is a research theme.
He has authored or co-authored over 65 publications in journals, as
well as many conference papers and book chapters. Some recent publications
appear in the IEEE Transactions on Pattern Analysis and Machine Intelligence, Neural Computation, Information Fusion, Journal of
Machine Learning research, IEEE Transactions on Systems, Man, and
Cybernetics, Pattern Recognition, the
International Conference on Pattern Recognition, the Multiple
Classifier Systems Workshop, and the FUZZ-IEEE conference. I co-edited the
1994 joint North American Fuzzy Information Processing Society
(NAFIPS),
IFIS and NASA conference proceedings and the 1998 proceedings. I
am a fellow of the IEEE. I'm a past president of NAFIPS.
Also, associate editor for the IEEE Transactions on Fuzzy
Systems,
International Journal of Approximate Reasoning, International Journal
of
Intelligent Data Analysis, and The Handbook of Fuzzy Logic.
I'm a Fellow of the IEEE.
I'm the former Editor-in-Chief for the IEEE
Transactions on Systems, Man and Cybernetics, Part B , please
consider submitting. I am the Jr. Past President of the
IEEE Systems, Man and Cybernetics Society . Please consider joining us.
Some papers on knowledge guided image segmentation
P. Hore, L.O. Hall, and D.B. Goldgof, A Scalable Framework For Cluster Ensembles, Pattern Recognition, 42 (2009), pp. 676-688
P. Hore, L.O. Hall, D.B. Goldgof, Y. Gu, A.A. Maudsley and
A. Darkazanli, A Scalable Framework For Segmenting Magnetic
Resonance Images Journal of Signal Processing Systems, (Online)
DOI10.1007/s11265-008-0243-1, Volume 54, Issue 1 (2009), Page 183-203.
A. A. Maudsley, A. Darkazanli, J. R. Alger, L. O. Hall,
N. Schuff, C. Studholme, Y. Yu, A. Ebel, A. Frew, D. Goldgof,
Y. Gu, R. Pagare, F. Rousseau, K. Sivasankaran, B. J. Soher,
P. Weber, K. Young and X. Zhu, Comprehensive processing, display and
analysis for in vivo MR spectroscopic imaging, NMR IN BIOMEDICINE
NMR Biomed, V. 19, 492-503, 2006.
Mingrui
Zhang and Lawrence O. Hall and Dmitry B. Goldgof, A Generic
Knowledge-Guided
Image Segmentation and Labeling System Using Fuzzy Clustering
Algorithms,
IEEE Transactions on Systems, Man, and Cybernetics, Part B,
http://ieeexplore.ieee.org/,
V. 32, No. 5, pp. 571-582, 2002.
S.
Eschrich, J. Ke, L.O. Hall and D.B. Goldgof, Fast Accurate Fuzzy
Clustering
through Data Reduction, IEEE Transactions on Fuzzy Systems, V. 11, 2,
pp. 262-270 2003.
Y. Gu, L. Hall, D. Goldgof, P. Kanade and F. Murtagh,
Sequence Tolerant Segmentation System of Brain MRI, IEEE International
Conference on Systems, Man and Cybernetics, pp. 2936-2943, Oct, 2005.
M.C.
Clark, L.O. Hall, D.B. Goldgof, R. Velthuizen, R. Murtagh, and M.S.
Silbiger, Automatic
Tumor Segmentation Using Knowledge-Based Techniques. IEEE Trans.
Medical
Imaging, V. 17, No. 2, pp. 187-201, 1998. (html)
In pdf
format.
M.C.
Clark, L.O. Hall, D.B. Goldgof, R. Velthuizen, R. Murtagh, and M.S.
Silbiger,
Unsupervised Brain Tumor Segmentation using Knowledge-Based Fuzzy
Techniques,
Fuzzy and Neuro-Fuzzy Systems in Medicine, Ed. H-N Teodorescu, A.
Kandel,
L.C. Jain, pp. 137-169, 1998. (pdf) Discusses segmenting and
identifying
brain tumors from the ventricles downward. Uses fuzzy edge detection.
In
pdf format.
Using
Adaptive Fuzzy Rules for Image Segmentation. FUZZ-IEEE'98 (html)
Cheng,
T.W.,
Goldgof, D.B. and Hall, L.O., Fast Fuzzy Clustering, Fuzzy Sets and
Systems,
V. 93, pp. 49-56, 1998. (pdf)
The
Case for Genetic Algorithms in Fuzzy Clustering. IPMU'98 (html)
Using
Fuzzy Information in Knowledge Guided Segmentation of Brain Tumors.
1995
IJCAI Workshop(html)
Knowledge
Based (Re-)Clustering, 12th IAPR International Conference on Pattern
Recognition,
1994 (html)
M.
Clark,
D. Goldgof, L.O. Hall, L. Clarke, M. Silbiger, C. Li, MRI Segmentation
Using Fuzzy Clustering Techniques: Integrating
Knowledge,
IEEE Eng. in Medicine & Biology v.13 no.5 pp.730-742 1994 (pdf)
Some papers on data mining and other related topics
K. Kramer, L.O. Hall, D.B. Goldgof and A. Remsen,
Fast Support Vector Machines for Continuous Data, IEEE
Transactions on Systems, Man and Cybernetics, Part B: Cybernetics,
To Appear.
P. Hore, L.O. Hall, and D.B. Goldgof, A Scalable Framework For Cluster Ensembles, Pattern Recognition, 42 (2009), pp. 676-688.
N. Chawla, D. A. Cieslak, L.O. Hall and A, Joshi, Automatically countering imbalance and its empirical relationship to cost, Data Mining and Knowledge Discovery, V. 17, No. 2, pp. 225-252, Aug., 2008.
P. Hore, L.0. Hall, D. Goldgof and W. Cheng,
Online Fuzzy C Means, NAFIPS, May, 2008.
J. Canul-Reich, L.O. Hall, D.B. Goldgof,
Feature Selection for Microarray Data by AUC Analysis, IEEE
International Conference on SMC, 2008.
J.N. Korecki, R.E. Banfield, L.O. Hall, K.W. Bowyer,
W.P. Kegelmeyer, Semi-supervised learning
on large complex simulations, International Conference on Pattern
Recognition, Dec. 2008.
L. Shoemaker, R.E. Banfield, L.O. Hall,
K.W. Bowyer, W.P. Kegelmeyer, Detecting and Ordering Salient
Regions for Efficient Browsing, International Conference on Pattern
Recognition, Dec. 2008
R.E. Banfield, L.O. Hall, K.W. Bowyer, and W. Philip,
Kegelmeyer, A Comparison of Decision Tree Ensemble Creation Techniques, IEEE Transactions on Pattern Analysis and Machine Intelligence, V. 29, No. 1, pp. 173-180, January 2007.
P.M. Kanade and L.O. Hall, Fuzzy Ants and Clustering, IEEE
Transactions on Systems, Man and Cybernetics, Part A, V. 37,
N. 5, pp. 758-769, 2007.
Li Chen, D.B. Goldgof, L.O. Hall and S. Eschrich, Noise-based
Feature Perturbation as a Selection Method for Microarray
Data, ISBRA 2007, Atlanta, May 2007.
Prodip Hore, Lawrence O. Hall and Dmitry B. Goldgof, Creating
Streaming Iterative Soft Clustering Algorithms, NAFIPS 07, San Diego, 2007.
Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer, and
W. Philip Kegelmeyer, Boosting Lite - Handling Larger Datasets and Slower Base Classifiers, Multiple Classifier Systems Conference, Prague, 2007.
Juana Canul-Reich, Larry Shoemaker and Lawrence O. Hall,
Ensembles of Fuzzy Classifiers, IEEE International Conference on
Fuzzy Systems, London, 2007.
Prodip Hore, Lawrence O. Hall, and Dmitry B. Goldgof, Single
Pass Fuzzy C Means, IEEE International Conference on
Fuzzy Systems, London, 2007
L. Shoemaker, R. E. Banfield, L.O. Hall,
K.W. Bowyer, and L.O. Hall, Learning to Predict Salient Regions from
Disjoint and Skewed Training Sets, International Conference on Tools
for Artificial Intelligence, Washington, D.C. 2006.
P. Hore, L.O. Hall, and D.B. Goldgof, A Cluster Ensemble
Framework for Large Data sets, IEEE International Conference on
Systems, Man and Cybernetics, Taipei, Taiwan, Oct. 2006.
Yong Zhang; Hall, L.O.; Goldgof, D.B.; Sarkar, S., A constrained genetic approach for computing material property of elastic objects, IEEE Transactions on Evolutionary Computation,
Volume 10, Issue 3, June 2006 Page(s):341 - 357.
Y. Gu and L.O. Hall,
Kernel Based Fuzzy Ant Clustering with Partition validity, IEEE International
Conference on Fuzzy Systems, pp. 263-267, Vancouver, Ca., July 2006.
Shibendra Pobi and L.O. Hall, Predicting Juvenile Diabetes from Clinical Test Results, International
Joint Conference on Neural Networks, pp. 4161-4167, Vancouver, Ca., July 2006.
T. Luo, K. Kramer, D.B. Goldgof, L.O. Hall, S. Samson, A. Remsen, T. Hopkins, Active Learning to Recognize Multiple Types of Plankton, Journal of Machine Learning Research 6(Apr):589--613, 2005.
R.E. Banfield, L.O. Hall, K.W. Bowyer, and W. Philip,
Kegelmeyer, Ensemble diversity measures and their application to
Thinning, Information Fusion, V. 6, pages 49-62, 2005.
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Ensembles of Classifiers from Spatially Disjoint Data, The Sixth International Conference on Multiple Classifier Systems, Monterey, CA, pp. 196-205, June 2005.
N.V. Chawla, L.O. Hall and A. Joshi, Wrapper-based Computation
and Evaluation of Sampling Methods for Imbalanced Datasets, Workshop
on Utility-Based Data Mining, KDD'05, Chicago, IL, August 2005.
L. Hall, T. Luo, D. Goldgof, A. Remsen,
"Bit Reduction Support Vector Machine",
IEEE International Conference on Data Mining, pp. 733-736,
Houston, Texas, November 2005. (longer version)
Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer,Learning ensembles from bites: A scalable and accurate
approach", Journal of Machine Learning Research, Vol
5, pp 421--451, April 2004.
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, Divya
Bhadoria, W. Philip Kegelmeyer and Steven Eschrich, A comparison of
Ensemble Creation Techniques, Fifth international workshop on multiple
classifier systems, Caligari Italy, June, pp. 223-232, 2004.
Xiaomei Liu, Lawrence O. Hall, and Kevin W. Bowyer, Comments on ``A parallel Mixture of SVMs for Very Large Scale Problems'', Neural Computation, vol. 16, No. 7, pp. 1345-1351, July, 2004.
X. Liu, K.W. Bowyer, and L.O. Hall,
Decision Trees Work Better Than Feed-Forward Back-Propagation
Neural Nets for A Specific Class of Problems, 2004 IEEE International
Conference on Systems, Man and Cybernetics, Hague, Netherlands.
Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott
Samson, Andrew Remsen, Thomas Hopkins, Active Learning to Recognize
Multiple Types of Plankton, International Conference on Pattern
Recognition, Cambridge, UK, 2004.
Parag M. Kanade and Lawrence O. Hall, Fuzzy ants clustering with centroids, FUZZ-IEEE'04, 2004.
P. Hore and L. O. Hall, Distributed Clustering for Scaling Classic Algorithms, FUZZ-IEEE, 2004.
Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield, Divya
Bhadoria, W. Philip Kegelmeyer and Steven Eschrich, Comparing Pure Parallel Ensemble Creation Techniques Against
Bagging , The Third IEEE International Conference on Data Mining,
Melbourne, Florida, pp. 533-536, November, 2003.
T.
Luo, K. Kramer, D. Goldgof, L.O. Hall, S. Samson, A. Remson, and T.
Hopkins, Learning to Recognize Plankton, IEEE International Confernece
on SMC, 2003.
N.V.
Chawla, T.E. Moore, Jr., L.O. Hall, K.W. Bowyer, W.P. Kegelmeyer and C.
Springer, Distributed Learning with Bagging-Like Performance, Pattern
Recognition Letters, Vol. 24 (1-3) pp. 455-471, 2003.
Lawrence O. Hall,
Kevin W. Bowyer, Robert E. Banfield, Steven Eschrich, Richard Collins,
Is Error-Based Pruning Redeemable?, International Journal on Artificial
Intelligence Tools V. 12, No. 3, pp. 249-264, 2003.
P.M.
Kanade and L.O. Hall, Fuzzy Ants as a Clustering Concept, 22nd
international
conference of the North American fuzzy information processing society
NAFIPS,
p. 227-232, 2003.
N.
Chawla,
K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, SMOTE: Synthetic Minority
Over-sampling
TEchnique, Journal of Artificial Intelligence Research, Volume 16,
pages
321-357, 2002.
N.
V. Chawla, L. O. Hall, K.W. Bowyer, T. E. Moore, Jr., and W. P.
Kegelmeyer,
Distributed Pasting of Small Votes, Multiple Classifier Systems
Conference,
Caligari, Italy, 2002.
Steven
Eschrich , Nitesh V. Chawla , Lawrence O. Hall, Generalization Methods
in Bioinformatics, BIOKDD02 Workshop at KDD'02, Edomonton, Ca., 2002.
L.O.
Hall,
R. Collins, K.W. Bowyer, and R. Banfield, Error-Based Pruning of
Decision
Trees Grown on Very Large Data Sets Can Work!, International Conference
on Tools for Artificial Intelligence, pp. 233-238, November 2002.
N.
Chawla,
S. Eschrich, and LO Hall, Creating Ensembles of Classifiers, IEEE Int.
Conf on Data Mining, Nov., pp. 580-581, 2001.
L.O.
Hall,
Rule Chaining in Fuzzy Expert Systems, IEEE Transactions on Fuzzy
Systems,
V. 9, No. 6, pp. 822-827, 2001. N.
Chawla, T.E. Moore, Jr., K.W. Bowyer, L.O. Hall, C. Springer, and W.P.
Kegelmeyer, Bagging Is A Small-Data-Set Phenomenon, IEEE Conf. on
Computer
Vision and Pattern Recognition, Hawaii, Dec., 2001.
Nitesh
Chawla, Thomas E. Moore, Jr., Kevin W.Bowyer, Lawrence O. Hall, Clayton
Springer, and Philip Kegelmeyer, Bagging-Like Effects for Decision
Trees
and Neural Nets in Protein Secondary Structure Prediction, Workshop on
Data Mining in Bioinformatics, (KDD01), pp. 50-59.
S.
Eschrich,
J. Ke, L. Hall, D. Goldgof, ``Fast Fuzzy Clustering of Infrared
Images",
20th NAFIPS International Conference, Vancouver, Canada July 2001, pp.
1145-1150.
M.
Zhang,
L.O. Hall, F.E. Muller-Karger, and D.B. Goldgof, Knowledge-Guided
Classification
of Coastal Zone Color Images off the West Florida Shelf, International
Journal of Pattern Recognition and Artificial Intelligence, V. 14, No.
8, 2000, pp. 987-1007.
L.O.
Hall,
K.W. Bowyer, W.P. Kegelmeyer, T.E. Moore and C. Chao, Distributed
Learning
on Very Large Data Sets, Workshop on Distributed and Parallel Knowledge
Discovery, (KDD00), pp. 79-84, Aug, 2000.
L.O. Hall,
N.
Chawla, K.W. Bowyer and W.P. Kegelmeyer, Learning Rules from
Distributed
Data, Workshop on Large-Scale Parallel KDD Systems, (KDD99), Also in
RPI,
CS Dept Tech Report 99-8, pp. 77-83.
Hall,
L.O.,
Ozyurt, I.B., and Bezdek, J.C., Clustering with a Genetically Optimized
Approach, IEEE Transactions on Evolutionary Computation, V. 3, No. 2,
pp.
103-112, 1999. Long version of paper in IEEE Trans. on EC. (pdf)
Decision
Tree
Learning on Very Large Data Sets in IEEE SMC Conference, 1998. (pdf)
Learning
in vision in JAIR, V.3, 1995, pp. 187-222. (html)
Averaged
Reward Fuzzy Reinforcement Learning Applied to Fuzzy Rule Tuning,
FUZZY'97
Conference, 1997 (postscript).
Some papers on Clinical Trial Assignment
S. Fefilatyev, L. Chen, T.V. Ivanovskiy,
Lawrence O. Hall and Dmitry B. Goldgof, H. Greenstein and C.R. Garrett, Complications in using automated methods
to increase clinical trial accrual, Intl. J Biomedical Engineering and Technology, To Appear.
E. Fink, P.K. Kokku, S. Nikiforou, L.O. Hall, D.B. Goldgof,
J.P. Krischer, Selection of Patients for Clinical Trials: An
Interactive Web-Based System, Artificial Intelligence in Medicine,
31(3), 241-254, July 2004.
Bhavesh D. Goswami, Lawrence O. Hall, Dmitry B. Goldgof,
Eugene Fink2, Jeffrey P. Krischer, Using Probabilistic Methods to
Optimize Data Entry in Accrual of Patients to Clinical Trials, IEEE
CBMS 2004.
Savvas
Nikiforou, Eugene Fink, Lawrence O. Hall, Dmitry B. Goldgof, and Jeffry
P. Krischer, Knowledge Acquisition for Clinical-Trial Selection, IEEE
International
Conference on Systems, Man and Cybernetics, October 2002.
Princeton
K. Kokku, Lawrence O. Hall, Dmitry B. Goldgof, Eugene Fink, and Jeffry
P. Krischer, A Cost-effective Agent for Clinical Trial Assignment, IEEE
International Conference on Systems, Man and Cybernetics, October 2002.
Fuzzy Rule Chaining
L.O.
Hall, Rule Chaining in Fuzzy Expert Systems, IEEE Transactions on
Fuzzy Systems, V. 9, No. 6, pp. 822-827, 2001.
The USF Computer Science and
Engineering
Department Page.
Mountain Climbing in 1999
Hiking Half Dome
Some pictures from the semifinals of my latest tennis tourney.
If you have a fast connection and full detail try these pics.
A good
second
serve.
Nailed
this backhand.