- University of South Florida
- Melchor Visiting Professor of Engineering, Residential Fellow (2015-2016)
- "Mining ‘Big Data’ for Small and Impactful Nuggets"
Lawrence O. Hall is a Distinguished University Professor in the Department of Computer Science and Engineering at the University of South Florida. His 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.
Hall has authored or co-authored over 85 publications in journals as well as many conference papers and book chapters. Recent publications have appeared in IEEE Access, Pattern Recognition, IEEE Transactions on Fuzzy Systems, and the Journal of Magnetic Resonance Imaging. According to Google Scholar his research work has been cited over 18,000 times. He has received over 5M in research funding from agencies such as the National Science Foundation, National Institutes of Health, Department of Energy, NASA, DARPA, etc.
He is a fellow of the IEEE, the AAAS, and IAPR. He received the Norbert Wiener award in 2012 from the IEEE SMC Society. He received the 2017 Theodore and Venette Askounes-Ashford Distinguished Scholar Award and 2017 IEEE SMC Joseph G. Wohl Award. In 2015 he was a Distinguished Visiting Professor at the University of Technology, Sydney. He received the IEEE SMC Society Outstanding contribution award in 2008. He received an Outstanding Research achievement award from the University of South Florida in 2004. He is a past president of NAFIPS, the former vice president for membership of the SMC society, and he was the President of the IEEE Systems, Man and Cybernetics society for 2006-7.
He was the Editor-In-Chief of the IEEE Transactions on Systems, Man and Cybernetics, Part B, 2002-05. He served as the first Vice President for Publications of the IEEE Biometrics Council. He is currently on the IEEE Publications and Services Products Board and Chairs its Strategic Planning Committee. He is also a member of the IEEE PRAC board and serves as associate editor for the International Journal of Intelligent Data Analysis, the International Journal of Pattern Recognition and Artificial Intelligence and International Journal of Approximate Reasoning. He is on the IEEE Access editorial board.
Active Cleaning of Label Noise
Pattern Recognition, 2016
Mislabeled examples in the training data can severely affect the performance of supervised classifiers. In this paper, we present an approach to remove any mislabeled examples in the dataset by selecting suspicious examples as targets for inspection. We show that the large margin and soft margin principles used in support vector machines (SVM) have the characteristic of capturing the mislabeled examples as support vectors. Experimental results on two character recognition datasets show that one-class and two-class SVMs are able to capture around 85% and 99% of label noise examples, respectively, as their support vectors. We propose another new method that iteratively builds two-class SVM classifiers on the non-support vector examples from the training data followed by an expert manually verifying the support vectors based on their classification score to identify any mislabeled examples. We show that this method reduces the number of examples to be reviewed, as well as providing parameter independence of this method, through experimental results on four data sets. So, by (re-)examining the labels of the selective support vectors, most noise can be removed. This can be quite advantageous when rapidly building a labeled data set.