environments for natural induction: STAR-AQ19/AQ21

(Michalski, Wojtusiak, Kaufman, Bloedorn, Fischthal, Rosenbach)

This project is concerned with developing an integrated system of programs for conducting experiments and developing applications in the areas of machine learning and data mining. The system STAR, being developed, aims at integrating AQ-based natural induction systems, such as AQ19 and AQ21, with a number of utility programs. The STAR system will include:

– AQ19/AQ21: Natural induction systems for generating attributional rulesets from examples
– ATEST1/ATEST2: A program for testing attributional rulesets on testing data using a number of strict and flexible rule-matching methods
– AQ17-DCI: A program for data-driven constructive induction using the AQ methodology.
– AQ17-HCI: A program for hypothesis-driven constructive induction using the AQ methodology.
– KV1/KV2: Programs for diagrammatic visualization of discrete data and attributional rules
– RT: A program for determining task-optimized decision trees from decision rules
– VARSEL: A program for selecting the most relevant attributes for a given learning task, using the PROMISE method
– ESEL: A program for selecting the most useful and representative examples for a given learning task
– MAR: A program for generating multihead attributional rules from single-head rules. Such rules can be viewed as a generalization of association rules.
– CAG: A program that visualizes attributional rules in the form of association graphs
– EPIC: A program for testing attributional rulesets on episodes (sequences of events viewed as a whole)

Selected References

Wojtusiak, J., Shiver, J., Ngufor, C. and Ewald, R., “Machine Learning in Hospital Billing Management,” Presentation at HIMSS 2011 Acedemic Forum (Hosted by AUPHA), Orlando, FL, February 20, 2011.

Wojtusiak, J. and Alemi, F., “Analyzing Decisions Using Datasets with Multiple Attributes: A Machine Learning Approach,” Handbook of Healthcare Delivery Systems, CRC Press, 2010.

Wojtusiak, J., Michalski, R. S., Simanivanh, T. and Baranova, A. V., “Towards application of rule learning to the meta-analysis of clinical data: An example of the metabolic syndrome,” International Journal of Medical Informatics, 4, 1, pp. 43-54, 2009.

Gehrke J. D. and Wojtusiak, J., “Traffic Prediction for Agent Route Planning,” Proceedings of the International Conference on Computational Science, Lecture Notes in Computer Science, Krakow, Poland, Springer, 2008.

Pietrzykowski, J. and Wojtusiak, J., “Learning Attributional Ruletrees,” Proceedings of the 16th International Conference Intelligent Information Systems, Zakopane, Poland, June 16-18, 2008.

Michalski, R. S. and Wojtusiak, J., “The Distribution Approximation Approach to Learning from Aggregated Data,” Reports of the Machine Learning and Inference Laboratory, MLI 08-2, George Mason University, Fairfax, VA, 2008.

Gehrke J. D. and Wojtusiak, J., “A Natural Induction Approach to Traffic Prediction for Autonomous Agent-based Vehicle Route Planning,” Reports of the Machine Learning and Inference Laboratory, MLI 08-1, George Mason University, Fairfax, VA, February 17, 2008.

Wojtusiak, J. and Michalski, R. S., “Analyzing Diaries for Analytical Relapse Prevention Using Natural Induction: A Method and Preliminary Results,” Quality Management in Health Care, 17, 80-89, 2008.

Wojtusiak, J., Michalski, R. S., Simanivanh, T. and Baranova, A. V., “The Natural Induction System AQ21 and Its Application to Data Describing Patients with Metabolic Syndrome: Initial Results,” Proceedings of the International Conference on Machine Learning and Applications, Cincinnati, OH, December 13-15, 2007.

Michalski, R. S. and Wojtusiak, J., “Generalizing Data in Natural Language,” Proceedings of the International Conference Rough Sets and Emerging Intelligent Systems Paradigms, RSEISP’07, Lecture Notes in Computer Science, Springer, 2007.

Michalski, R. S. and Wojtusiak, J., “Semantic and Syntactic Attribute Types in AQ Learning,” Reports of the Machine Learning and Inference Laboratory, MLI 07-1, George Mason University, Fairfax, VA, 2007.

Wojtusiak, J., Michalski, R. S., Kaufman, K. and Pietrzykowski, J., “The AQ21 Natural Induction Program for Pattern Discovery: Initial Version and its Novel Features,” Proceedings of The 18th IEEE International Conference on Tools with Artificial Intelligence, Washington D.C., November 13-15, 2006.

Wojtusiak, J. and Michalski, R. S., “The Use of Compound Attributes in AQ Learning,” Proceedings of the Intelligent Information Processing and Web Mining Conference, IIPWM 06, Ustron, Poland, June 19-22, 2006.

Michalski, R. S., Kaufman, K., Pietrzykowski, J., Wojtusiak, J., Mitchell, S. and Seeman, W.D., “Natural Induction and Conceptual Clustering: A Review of Applications,” Reports of the Machine Learning and Inference Laboratory, MLI 06-3, George Mason University, Fairfax, VA, June, 2006.

Wojtusiak, J., Michalski, R. S., Kaufman, K. and Pietrzykowski, J., “Multitype Pattern Discovery Via AQ21: A Brief Description of the Method and Its Novel Features,” Reports of the Machine Learning and Inference Laboratory, MLI 06-2, George Mason University, Fairfax, VA, 2006.

Michalski, R.S. and Wojtusiak, J., “Reasoning with Meta-values in AQ Learning,” Reports of the Machine Learning and Inference Laboratory, MLI 05-1, George Mason University, Fairfax, VA, June, 2005.

Michalski, R.S., “Generating Alternative Hypotheses in AQ Learning,” Reports of the Machine Learning and Inference Laboratory, MLI 04-6, George Mason University, Fairfax, VA, December, 2004.

Wojtusiak, J., “AQ21 User’s Guide,” Reports of the Machine Learning and Inference Laboratory, MLI 04-3, George Mason University, Fairfax, VA, September, 2004 (updated in September, 2005).

Michalski, R.S., ATTRIBUTIONAL CALCULUS: A Logic and Representation Language for Natural Induction,” Reports of the Machine Learning and Inference Laboratory, MLI 04-2, George Mason University, Fairfax, VA, April, 2004.

Michalski, R.S. and Kaufman, K.A., “The AQ19 System for Machine Learning and Pattern Discovery: A General Description and User’s Guide,” Reports of the Machine Learning and Inference Laboratory, MLI 01-2, George Mason University, Fairfax, VA, 2001.

Michalski, R.S. and Kaufman, K, “Learning Patterns in Noisy Data: The AQ Approach,” in Paliouras, G., arkaletsis, V. and Spyropoulos, C. (eds.) Machine Learning and its Applications, Springer-Verlag, pp. 22-38, 2001.

Kaufman, K.A. and Michalski, R.S., “Learning from Inconsistent and Noisy Data: The AQ18 Approach,” Proceedings of the Eleventh International Symposium on Methodologies for Intelligent Systems, Warsaw, pp. 411-419, June 8-11, 1999.

Michalski, R.S. and Kaufman, K.A., “A Measure of Description Quality for Data Mining and its Implementation in the AQ18 Learning System,” Proceedings of the ICSC Congress on Computational Intelligence Methods and Applications (CIMA-99), Rochester, NY, pp. 369-375, June, 1999.

Michalski, R.S. and Kaufman, K.A., “Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach,” in Michalski, R.S., Bratko, I. and Kubat, M. (Eds.), Machine Learning and Data Mining: Methods and Applications, London: John Wiley & Sons, pp. 71-112, 1998.

Zhang, Q., “Knowledge Visualizer: A Software System for Visualizing Data, Patterns and Their Relationships,” Reports of the Machine Learning and Inference Laboratory, MLI 97-14, George Mason University, Fairfax, VA, September, 1997.

Michalski, R.S. and Imam, I.F., “On Learning Decision Structures,” Fundamenta Matematicae, 31(1), dedicated to the memory of Dr. Cecylia Raucher, Polish Academy of Sciences, pp. 49-64, 1997.

Kaufman, K. and Michalski, R.S., “A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System,” Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, OR, August, 1996, pp. 232-237.

For more references, see publications section.