Gary M. Weiss's Publications

Journal Articles

1. Gary M. Weiss and Ye Tian (2008). Maximizing Classifier Utility when there are Data Acquisition and Modeling Costs. Data Mining and Knowledge Discovery, 17(2): 253-282. (abstract)
2. Gary M. Weiss and Ye Tian (2006). Maximizing Classifier Utility when Training Data is Costly, SIGKDD Explorations 8(2):31-38.
3. Gary M. Weiss (2004). Mining with Rarity: A Unifying Framework, SIGKDD Explorations 6(1):7-19.
4. Gary M. Weiss and Foster Provost (2003). Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction, Journal of Artificial Intelligence Research, 19:315-354.
5. Gary. M. Weiss and Johannes P. Ros (1998). Implementing Design Patterns with Object-Oriented Rules, Journal of Object-Oriented Programming, 11(7): 25-35, SIGS Publications Inc, New York.

Book Chapters

1. Gary M. Weiss and Brian Davison (2009). Data Mining. In H. Bidgoli (ed.), Handbook of Technology Management, John Wiley and Sons, expected Nov. 2009.
2. Gary M. Weiss (2008). Data Mining in the Telecommunications Industry. In J. Wang (ed.), Encyclopedia of Data Warehousing and Mining, Second Edition, Information Science Publishing.
3. Tamraparni Dasu and Gary M. Weiss (2008). Mining Data Streams. In J. Wang (ed.), Encyclopedia of Data Warehousing and Mining, Second Edition, Information Science Publishing.
4. Gary M. Weiss (2005). Mining Rare Cases. In O. Maimon and L. Rokach(eds.), Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Kluwer Academic Publishers, 765-776.
5. Gary M. Weiss (2005). Data Mining in Telecommunications. In O. Maimon and L. Rokach(eds.), Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Kluwer Academic Publishers, 1189-1201.
6. Gary M. Weiss (2002). Predicting Telecommunication Equipment Failures from Sequences of Network Alarms. In W. Kloesgen and J. Zytkow (eds.), Handbook of Knowledge Discovery and Data Mining, Oxford University Press, 891-896.
7. Gary M. Weiss, John Eddy, & Sholom Weiss (1998). Intelligent Telecommunication Technologies, Knowledge-based Intelligent Techniques (chapter 8), L. C. Jain, editor, CRC Press, 249-275.

Edited Works and Editorials

1. Gary M. Weiss, Bianca Zadrozny, and Maytal Saar-Tsechansky (2008). Special Issue on Utility-Based Data Mining (editors), Data Mining and Knowledge Discovery, 17(2).
2. Gary M. Weiss, Bianca Zadrozny, and Maytal Saar-Tsechansky (2008). Guest editorial: special issue on utility-based data mining. Data Mining and Knowledge Discovery, 17(2): 129-135.
3. Bianca Zadrozny, Gary. M. Weiss and Maytal Saar-Tsechansky (2006). Proceedings of the Second International Workshop on Utility-Based Data Mining (editors). ACM Press, Philadelphia, PA, August 2006.
4. Bianca Zadrozny, Gary. M. Weiss and Maytal Saar-Tsechansky (2006). UBDM 2006: Utility-Based Data Mining 2006 Workshop Report. SIGKDD Explorations, 8(2), ACM Press, December 2006.
5. Gary. M. Weiss, Maytal Saar-Tsechansky and Bianca Zadrozny (2005). Report on UBDM-05: Workshop on Utility-Based Data Mining. SIGKDD Explorations, 7(2):145-147, ACM Press, December 2005.
6. Gary. M. Weiss, Maytal Saar-Tsechansky and Bianca Zadrozny (2005). Proceedings of the First International Workshop on Utility-Based Data Mining (editors). ACM Press, Chicago, IL, August 2005.

Conference Papers

1. Gary M. Weiss, Kate McCarthy and Bibi Zabar (2007). Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?, Proceedings of the 2007 International Conference on Data Mining, CSREA Press, 35-41.
2. Gary M. Weiss and Haym Hirsh (2000). A Quantitative Study of Small Disjuncts. Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000), AAAI Press, Menlo Park, CA, 665-670. An expanded version is also available.
3. Gary M. Weiss (1999). Timeweaver: a Genetic Algorithm for Identifying Predictive Patterns in Sequences of Events. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), Morgan Kaufmann, San Francisco, CA, 718-725.
4. Gary M. Weiss and Haym Hirsh (1998). Learning to Predict Rare Events in Event Sequences, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), AAAI Press, Menlo Park, CA, 359-363.
5. Gary M. Weiss, Johannes P. Ros, Anoop Singhal (1998). ANSWER: Network Monitoring Using Object-Oriented Rules, Proceedings of the Tenth Conference on Innovative Applications of Artificial Intelligence (IAAI-98), AAAI Press, Menlo Park, CA, 1087-1093.
6. Gary M. Weiss and Haym Hirsh (1998). The Problem with Noise and Small Disjuncts, Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98). Morgan Kaufmann, San Francisco, CA, 574-578.
7. Dan Dvorak, Anil Mishra, Johannes Ros, Gary M. Weiss & Diane Litman (1996). R++: Using Rules in Object-Oriented Designs, in Addendum Object-Oriented Programming Systems, Languages and Applications (OOPSLA) San Jose, CA.
8. Gary M. Weiss (1995). Learning with Rare Cases and Small Disjuncts, Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, California, 558-565.

Workshop Papers

1. Ye Tian, Gary M. Weiss and Qiang Ma (2007). A Semi-Supervised Approach for Web Spam Detection using Combinatorial Feature-Fusion, Proceedings of the ECML/PKDD 2007 Graph Labelling Workshop and Web Spam Challenge (at ECML/PKDD 07), 16-23.
2. Ye Tian, Gary M. Weiss, D. Frank Hsu, and Qiang Ma (2007). A Combinatorial Fusion Method for Feature Mining, Proceedings of the First International Workshop on Mining Multiple Information Sources (at KDD-07), ACM Press, 6-13.
3. Gary. M. Weiss and Ye Tian (2006). Maximizing Classifier Utility when Training Data is Costly, Proceedings of the Second International Workshop on Utility-Based Data Mining (at KDD-06), Philadelphia, PA, ACM Press, 3-11.
4. Michelle Ciraco, Michael Rogalewski and Gary. M. Weiss (2005). Improving Classifier Utility by Altering the Misclassification Cost Ratio, Proceedings of the First International Workshop on Utility-Based Data Mining (at KDD-05), ACM Press, 46-52.
5. Kate McCarthy, Bibi Zabar and Gary. M. Weiss (2005). Does Cost-Sensitive Learning Beat Sampling for Classifying Rare Classes?, Proceedings of the First International Workshop on Utility-Based Data Mining (at KDD-05), ACM Press, 69-75.
6. Gary M. Weiss and Haym Hirsh (2000). Learning to Predict Extremely Rare Events. Papers from the AAAI Workshop on Learning from Imbalanced Data Sets, Technical Report WS-00-05, AAAI Press, Menlo Park, CA, 64-68.
7. Gary M. Weiss (1999). Mining Predictive Patterns in Sequences of Events. Presented at the 1999 AAAI/GECCO Workshop on Data Mining with Evolutionary Algorithms: Research Directions.
8. Gary M. Weiss and Haym Hirsh (1998). Event Prediction: Learning from Ambiguous Examples. Presented at the 1998 Neural Information Processing Systems (NIPS) Workshop on Learning from Ambiguous and Complex Examples.
9. Gary M. Weiss and Haym Hirsh (1998). Learning to Predict Rare Events in Categorical Time-Series Data, Papers from the AAAI Workshop on Predicting the Future: AI Approaches to Time-Series Problems, Technical Report WS-98-07, AAAI Press, Menlo Park, CA, 83-90.
10. Anoop Singhal, Gary M. Weiss and Johannes Ros (1996). A Model Based Reasoning Approach to Network Monitoring, Proceedings of the ACM Workshop of Databases for Active and Real Time Systems (DART) Rockville, Maryland, 41-44.

Ph.D. Disseration

1. Gary M. Weiss (2003). The Effect of Small Disjuncts and Class Distribution on Decision Tree Learning, Ph.D. Dissertation, Department of Computer Science, Rutgers University, New Brunswick, New Jersey, May 2003. (167 pages, 667 KB).