Publications

Papers Submitted Or Under Revision

Journals

1. W. H. Hsu, S. M. Gustafson, S. J. Harmon, E. Rodríguez, and C. A. Zhong. Incremental Learning Strategies for Genetic Programming-Based Multi-Agent Systems. Under revision for Genetic Programming and Evolvable Machines (GPEM), Kluwer. First submitted 2002.

2. W. H. Hsu. Bayesian Network tools in Java (BNJ): An Extendable Software Library for Graphical Models of Probability. Under revision for Journal of Online Mathematics and its Applications (JOMA). First submitted 2003.

Refereed Conference and Workshop Papers

3. S. J. Harmon, E. Rodríguez, C. A. Zhong, and W. H. Hsu. Empirical Comparison of Incremental Learning Strategies for Genetic Programming-Based Keep-Away Soccer Agents. Submitted to the 2004 AAAI Fall Symposium.

4. S. J. Harmon, E. Rodríguez, C. A. Zhong, and W. H. Hsu. Reuse Strategies for in Incremental Reinforcement Learning using Genetic Programming. Submitted to the 2004 European Conference on machine Learning (ECML-2004).

Published Papers

Journals

[Hs04] W. H. Hsu. Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning. Information Sciences, 163(1-3):103-122.

[HWRC02] W. H. Hsu, M. Welge, T. Redman, and D. Clutter. High-Performance Commercial Data Mining: A Multistrategy Machine Learning Application. Knowledge Discovery and Data Mining, 6(4):361-391. Kluwer Academic Publishers, 2002.

[HRW00] W. H. Hsu, S. R. Ray, and D. C. Wilkins. A Multistrategy Approach to Classifier Learning from Time Series. Machine Learning, 38(1-2):213-236. Kluwer Academic Publishers, 2000. (PostScript .ps.gz)

[RH98] S. R. Ray and W. H. Hsu.  Self-Organized-Expert Modular Network for Classification of Spatiotemporal Sequences. Intelligent Data Analysis, 2(4). IOS Press, October, 1998. (PostScript .ps.gz)

[HZ95] W. H. Hsu and A. E. Zwarico.  Automatic Synthesis of Compression Techniques for Heterogeneous Files.  Software: Practice and Experience, 25(10):1097-1116. Wiley, 1995. (PostScript .ps.gz)

Book Chapters

[Hs03] W. H. Hsu. Control of Inductive Bias in Supervised Learning using Evolutionary Computation: A Wrapper-Based Approach. In J. Wang, editor, Data Mining: Opportunities and Challenges, p. 27-54. IDEA Group Publishing. (PostScript .ps.gz)

Edited Proceedings

[HJP03] W. H. Hsu, R. Joehanes, and C. D. Page. Working Notes of the Workshop on Learning Graphical Models for Computational Genomics (MD-1), International Joint Conference on Artificial Intelligence (IJCAI-03). Acapulco, MEXICO, 09 August 2003. Available from URL: http://www.kddresearch.org/KDD/Workshops/IJCAI-2003-Bioinformatics/.

[GHHS02] H. Guo, E. Horvitz, W. H. Hsu, and E. Santos (editors). Working Notes of the Joint Workshop on Real-Time Decision Support and Diagnosis, AAAI/UAI/KDD-2002. Edmonton, Alberta, CANADA, 29 Jul 2002. Available from URL: http://www.kddresearch.org/Workshops/RTDSDS-2002.

[HKLS01] W. H. Hsu, H. Kargupta, H. Liu, and W. N. Street (editors). Working Notes of the Workshop on Wrappers for Performance Enhancement in Knowledge Discovery in Databases (KDD), International Joint Conference on Artificial Intelligence (IJCAI-01). Seattle, WA, 04 August 2001. Available from URL: http://www.kddresearch.org/KDD/Workshops/IJCAI-2001/.

Refereed Conference and Workshop Papers

[HJ04] W. H. Hsu and R. Joehanes. Permutation Genetic Algorithms for Score-Based Bayesian Network Structure Learning. In Proceedings of the International Conference on Computing, Communications and Control Technologies (CCCT-2004), Austin, TX, to appear.

[HHRZ04] W. H. Hsu, S. J. Harmon, E. Rodríguez, C. A. Zhong. Empirical Comparison of Incremental Reuse Strategies in Genetic Programming for Keep-Away Soccer (Late-Breaking Paper). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), Seattle, WA, to appear.

[HBJ03a] W. H. Hsu, P. Boddhireddy, and R. Joehanes. Using Probabilistic Relational Models for Collaborative Filtering. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Statistical Learning of Relational Models (SLR). Acapulco, MEXICO, August 2003.

[HJ03b] W. H. Hsu and R. Joehanes. Learning the structure of graphical models of gene regulation from microarray data: survey and experiments. In Working Notes of the Workshop on Learning Graphical Models for Computational Genomics (MD-1), International Joint Conference on Artificial Intelligence (IJCAI-03), Acapulco, MEXICO, 2003.

[HJ03a] W. H. Hsu and R. Joehanes. Bayesian Network tools in Java (BNJ) v2.0, In Proceedings of the American Society for Engineering Education (ASEE) Midwest Conference, 2003.

[Gu02] H. Guo. A Bayesian Metareasoner for Algorithm Selection for Real-time Bayesian Network Inference Problems. Doctoral Consortium Abstract, AAAI-2002, Edmonton, Alberta, Canada.

[GPSH02] H. Guo, B. B. Perry, J. A. Stilson, W. H. Hsu. A Genetic Algorithm for Tuning Variable Orderings in Bayesian Network Structure Learning. Student Abstract, AAAI-2002, Edmonton, Alberta, Canada.

[HG02] W. H. Hsu and S. M. Gustafson. Genetic Programming and Multi-Agent Layered Learning by Reinforcements. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, NY, 2002. (PostScript .ps.gz)

[HGPS02] W. H. Hsu, H. Guo, B. B. Perry, and J. A. Stilson. A Permutation Genetic Algorithm for Variable Ordering in Learning Bayesian Networks from Data. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, NY, 2002. (PostScript .ps.gz)

[GH01] S. M. Gustafson and W. H. Hsu. Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem. In Proceedings of the 4th European Conference on Genetic Programming (EuroGP-2001), Lake Como (Milan), Italy, April, 2001. Springer-Verlag, 2001. (PostScript .ps.gz)

[GH00] S. M. Gustafson and W. H. Hsu. Genetic Programming for Strategy Learning in Soccer-Playing Agents: A KDD-Based Architecture. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000) Workshop Program, Las Vegas, NV, July, 2000. (PostScript .ps.gz)

[HAP+99] W. H. Hsu, L. S. Auvil, W. M. Pottenger, D. Tcheng, and M. Welge. Self-Organizing Systems for Knowledge Discovery in Databases.  In Proceedings of the International Joint Conference on Neural Networks (IJCNN-99), Washington, DC, July, 1999. (PostScript .ps.gz)

[HR99] W. H. Hsu and S. R. Ray.  Construction of Recurrent Mixture Models for Time Series Classification.  In Proceedings of the International Joint Conference on Neural Networks (IJCNN-99), Washington, DC, July, 1999. (PostScript .ps.gz)

[HWWY99a] W. H. Hsu, M. Welge, J. Wu, and T. Yang.  Genetic Algorithms for Selection and Partitioning of Attributes in Large-Scale Data Mining Problems.  In Proceedings of the Joint AAAI-GECCO Workshop on Data Mining with Evolutionary Algorithms, Orlando, FL, July, 1999. (PostScript .ps.gz)

[GHVW98] E. Grois, W. H. Hsu, M. Voloshin, and D. C. Wilkins. Bayesian Network Models for Automatic Generation of Crisis Management Training Scenarios.  In Proceedings of the Tenth Innovative Applications of Artificial Intelligence Conference (IAAI-98), pp. 1113-1120.  Madison, WI, July, 1998. (PostScript .ps.gz)

[HGL+98a] W. H. Hsu, N. D. Gettings, V. E. Lease, Y. Pan, and D. C. Wilkins.  Crisis Monitoring: Methods for Heterogeneous Time Series Learning.  In Proceedings of the International Workshop on Multistrategy Learning (MSL-98). Milan, Italy, June, 1998. (PostScript .ps.gz)

[HGL+98b] W. H. Hsu, N. D. Gettings, V. E. Lease, Y. Pan, and D. C. Wilkins. Heterogeneous Time Series Learning for Crisis Monitoring.  In A. Danyluk, T. Fawcett, and F. Provost, editors, Proceedings of the Joint AAAI-ICML Workshop on AI Approaches to Time Series Problems, pp. 34-41.  Madison, WI, July, 1998. (PostScript .ps.gz)

[HR98b] W. H. Hsu and S. R. Ray.  Quantitative Model Selection for Heterogeneous Time Series. In R. Engels, F. Verdenius, and D. Aha, editors, Proceedings of the Joint AAAI-ICML Workshop on the Methodology of Applying Machine Learning, pp. 8-12.  Madison, WI, July, 1998. (PostScript .ps.gz)

[Hs97a] W. H. Hsu. A Position Paper on Statistical Inference Techniques Which Integrate Bayesian and Stochastic Neural Network Models.  In Proceedings of the International Conference on Neural Networks (ICNN-97), pp. 1972-1977.  Houston, TX, June, 1997. (PostScript, no figures .ps.gz, no figures)

[DKGH93a] A. Delcher, S. Kasif, H. Goldberg, W. Hsu. Probabilistic Prediction of Protein Secondary Structure Using Causal Networks. In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), pp. 316-321.  Washington, DC, August, 1993. 

[DKGH93b] A. Delcher, S. Kasif, H. Goldberg, W. Hsu. Prediction of Protein Secondary Fold Using Probabilistic Networks. In Proceedings of the First International Conference on Intelligent Systems for Molecular Biology (ISMB-93).  Bethesda, MD, July, 1993. 

Short Abstracts (Refereed Poster Papers)

[Hs04] W. H. Hsu. Relational Graphical Models of Computational Workflows for Data Mining (poster abstract). In Proceedings of the International Conference on Semantics of a Networked World: Semantics for Grid Databases (ICSNW-2004), Paris, FRANCE, to appear.

[HRZH04] S. J. Harmon, E. Rodríguez, C. A. Zhong, and W. H. Hsu. A Comparison of Hybrid Incremental Reuse Strategies for Reinforcement Learning in Genetic Programming (poster abstract). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), Seattle, WA, to appear.

[HBJ03b] W. H. Hsu, P. Boddhireddy, and R. Joehanes. DESCRIBER: Graphical Relational Models for Collaborative Filtering in Microarray Data Mining (poster abstract). Presented at The International Conference on Intelligent Systems for Molecular Biology. Brisbane, AUSTRALIA, 2003. Poster acceptance rate: 50%

[HDR03] W. H. Hsu, Y. Deng, and J. L. Roe (2003). A Software Toolkit for Learning Dynamic Graphical Models of Gene Regulatory Structure from Microarray Data (poster abstract). Presented at The International Conference on Intelligent Systems for Molecular Biology. Brisbane, AUSTRALIA, 2003. Poster acceptance rate: 50%

[GH03] H. Guo and W. H. Hsu. GA-Hardness Revisited (poster abstract). In Preceedings of the Genetic and Evolutionary Computation Conference (GECCO-2003), Chicago, IL, 2003.

[DGVH02] S. Das, S. Gosavi, S. Vaze, and W. H. Hsu. An Ant Colony Approach for the Steiner Tree Problem (poster abstract). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, NY, 2002. (PostScript.ps.gz)

[HSL02] W. H. Hsu, C. P. Schmidt, and J. A. Louis. Genetic Algorithm Wrappers for Feature Subset Selection in Supervised Inductive Learning (poster abstract). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, NY, 2002. (PostScript .ps.gz)

[HCGG00] W. H. Hsu, Y. Cheng, H. Guo, and S. Gustafson. Genetic Algorithms for Reformulation of Large-Scale KDD Problems with Many Irrelevant Attributes (poster abstract). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), Las Vegas, NV, July, 2000. Morgan Kaufmann Publishers, San Mateo, CA, 2000. (PostScript .ps.gz)

[HAR+99] W. H. Hsu, L. S. Auvil, T. Redman, D. Tcheng, and M. Welge. High-Performance Knowledge Discovery and Data Mining Systems Using Workstation Clusters (poster abstract). Presented at National Conference on High Performance Networking and Computing (SC99), Portland, OR, November, 1999. (PostScript .ps.gz)

[HWWY99b] W. H. Hsu, M. Welge, J. Wu, and T. Yang. Genetic Algorithms for Synthesis of Attributes in Large-Scale Data Mining (poster abstract).  In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), Orlando, FL, July, 1999. (PostScript .ps.gz)

[HR98a] W. H. Hsu and S. R. Ray.  A New Mixture Model for Concept Learning From Time Series (Extended Abstract). In A. Danyluk, T. Fawcett, and F. Provost, editors, Proceedings of the Joint AAAI-ICML Workshop on AI Approaches to Time Series Problems, pp. 42-43.  Madison, WI, July, 1998. (PostScript .ps.gz)

[Hs97b] W. H. Hsu. Probabilistic Learning in Bayesian and Stochastic Neural Networks (Doctoral Consortium Abstract). In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), p. 810. Providence, RI, July, 1997. (PostScript .ps.gz)

Theses

[Hs98] W. H. Hsu.  Time Series Learning With Probabilistic Network Composites.  Ph.D. thesis, University of Illinois at Urbana-Champaign (Technical Report UIUC-DCS-R2063).  August, 1998. (PDF PostScript .ps.gz)