## Publications |

## Papers Submitted Or Under Revision## Journals1. 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 Papers3. 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.
[HRW00] W. H. Hsu, S. R. Ray, and D. C. Wilkins.
A
Multistrategy Approach to Classifier Learning from Time Series. [RH98] S. R. Ray and W. H. Hsu.
Self-Organized-Expert
Modular Network for Classification of Spatiotemporal Sequences. [HZ95] W. H. Hsu and A. E. Zwarico.
Automatic
Synthesis of Compression Techniques for Heterogeneous Files.
## Book Chapters[Hs03] W. H. Hsu.
Control
of Inductive Bias in Supervised Learning using Evolutionary Computation:
A Wrapper-Based Approach. In J. Wang, editor, ## 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
[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 [GH01] S. M. Gustafson and W. H. Hsu.
Layered
Learning in Genetic Programming for a Cooperative Robot Soccer Problem.
In [GH00] S. M. Gustafson and W. H. Hsu.
Genetic
Programming for Strategy Learning in Soccer-Playing Agents: A KDD-Based
Architecture. In [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 [HR99] W. H. Hsu and S. R. Ray.
Construction
of Recurrent Mixture Models for Time Series Classification. In
[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 [GHVW98] E. Grois, W. H. Hsu, M. Voloshin, and D. C. Wilkins.
Bayesian
Network Models for Automatic Generation of Crisis Management Training
Scenarios. In [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
[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, [HR98b] W. H. Hsu and S. R. Ray.
Quantitative
Model Selection for Heterogeneous Time Series. In R. Engels, F.
Verdenius, and D. Aha, editors, [Hs97a] W. H. Hsu.
A
Position Paper on Statistical Inference Techniques Which Integrate
Bayesian and Stochastic Neural Network Models. In [DKGH93a] A. Delcher, S. Kasif, H. Goldberg, W. Hsu. Probabilistic
Prediction of Protein Secondary Structure Using Causal Networks. In
[DKGH93b] A. Delcher, S. Kasif, H. Goldberg, W. Hsu. Prediction of
Protein Secondary Fold Using Probabilistic Networks. In ## 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 [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 [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 [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 [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 [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, [Hs97b] W. H. Hsu.
Probabilistic
Learning in Bayesian and Stochastic Neural Networks (Doctoral
Consortium Abstract). In ## Theses[Hs98] W. H. Hsu. |