2006 INFORMS Workshop on Artificial Intelligence and Data Mining

The Artificial Intelligence (AI) and Data Mining (DM) Subdivisions of the Institute for Operations Research and Management Sciences (INFORMS) jointly organized a One-day Pre-conference Workshop in conjunction with the 2006 INFORMS Annual Conference in Pittsburgh, PA. The Workshop took place all day on November 4, 2006, prior to the INFORMS Annual Meeting.

Both the College of AI and the DM Section are interdisciplinary subdivisions that bring together researchers and practitioners in engineering and business that are interested in the theory, application and methodologies of AI, DM and knowledge discovery. More information about the College of AI can be found at http://www.informs.org/Subdiv/Section/ai.html, and more information about the DM Section can be found at http://dm.section.informs.org/.

The Workshop Organizing Committee consists of members from the AI and DM subdivisions:

Program Chairs: Tory Chen and Andrew Kusiak

Workshop Committee: Haldun Aytug, Wei Jiang, Siggi Olafsson, George Runger, Riyaz Sikora, Janet Twomey

Download Workshop Print Program

Panel on Research and Funding Opportunities

Abhijit Deshmukh, National Science Foundation (presentation)

Wendy Martinez, Office of Naval Research (presentation)

Michael Vannier, University of Chicago Medical Center (presentation)

Tutorial

George Runger, Arizona State University

Wei Jiang, Stevens Institute of Technology (presentation)

Andrew Kusiak, University of Iowa (presentation)

Track 1: Novel Methods in Learning and Data Mining

Technical Session A1: Advances in Learning

1. Genetic Algorithm Based Learning Using Feature Construction

Selwyn Piramuthu, Riyaz Sikora

2. Learning Optimal Parameter Values in Dynamic Environment: An Experiment with Softmax Reinforcement Learning Algorithms

Riyaz Sikora

3. Using Genetic Algorithms to Solve the Strategic Learning Problem

Fidan Boylu, Haldun Aytug, Gary Koehler

4. Some Recent Results on the Performance and Implementation of Manifold Learning Algorithms

Xiaoming Huo

Technical Session B1: Unsupervised Methods

1. Irregularity Analysis in Time Series Data

Tom Au, Winnie Duan, Wei Jiang

2. Using Clustering to Improve Sales Forecasts in Retail Merchandizing

Mahesh Kumar

3. A Novel Approach to Classification in Financial Applications

Marco Better, Fred Glover, Gary Kochenberger, Haibo Wang

4. Entropy Maximizing Density Estimation Using a Genetic Algorithm

Parag Pendharkar, Jim Rodger

Technical Session C1: Support Vector Machines

5. Solving Discrete Support Vector Machines with Tabu Search

Stefan Lessmann, Stefan Voß

6. Adjusted Support Vector Machines Based on a New Loss Function

Shuchun Wang, Kwok Tsui, Wei Jiang

7. Time Series Classification by Discrete Support Vector Machines

Carlotta Orsenigo, Carlo Vercellis

8. Hierarchical Local Clustering for Constraint Reduction in Rank-Optimizing Linear Programs

Kaan Ataman, Nick Street

Track 2: Applications and Applied Methods

Technical Session A2: Bioinformatics and Methods for Biomedical Applications

9. Image Denoising via Solution Paths

Li Wang, Ji Zhu

10. A Bayesian Approach for the Alignment of High-Resolution NMR Spectra

Seoung Kim, Zhou Wang, Carlos Duran

11. Disparate Data Fusion for Protein Phosphorylation Prediction

Genetha Gray, Pam Williams, Ken Sale

12. Solving a Mixed-Integer Programming Formulation of a Multi-Category Constrained Discrimination Model

Paul Brooks, Eva Lee

Technical Session B2: Interfacing Learning and Operations Research for Business and Industry

13. A Method for Reconciling Values of Parameters

Shinya Kikuchi, Manoj Jha

14. Improving the Estimation of Random Coefficient Logit Models of Demand

Marietta Tretter

15. Efficient Computer Experiment Based Optimization through Variable Selection

Thomas Shih, Venkata Pilla, Seoung Kim, Jay Rosenberger, Tory Chen

16. Modern Machine Learning for Automatic Optimization Algorithm Selection

Patty Hough, Pam Williams

Technical Session C2: Advances in Data Mining for Manufacturing

17. Time-Based Detection of Changes to Multivariate Patterns

Jing Hu, George Runger

18. Knowledge Discovery to Support Product Family Design

Seung Ki Moon, Timothy Simpson, Soundar Kumara

19. Improving Productivity in Manufacturing Environments Using Data Mining

Pam Ajoku, Bart Nnaji

20. Discovering Service Inventory Demand Patterns from Archetypal Demand Training Data

Gene Beardslee, Ted Trafalis