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Gary M. Weiss's Publications (by Research Area)

You may also view the publications by: type, year, or most popular. Click on the title to view the publication in pdf format. Papers with at least 25 citations are tagged (in red) with the approximate number of citations and clicking on the tag will bring up the list of citing papers.

Research areas: Utility-Based Data Mining (14), Rarity and Small Disjuncts (10), Event Prediction/Data Streams (7), Telecommunications (6), General Data Mining (5), Expert Systems and Object Technology (4), Feature Construction/Information Fusion (3), Semi-Supervised Learning (2), Link/Web Mining (1), Genetic Algorithms (1).

Utility-Based Data Mining

  1. Jack Chongjie Xue and Gary M. Weiss (2009). Quantification and Semi-Supervised Classification Methods for Handling Changes in Class Distribution, Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-09), ACM Press, 897-905.
  2. 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)
  3. Gary M. Weiss, Maytal Saar-Tsechansky, and Bianca Zadrozny (2008). Special Issue on Utility-Based Data Mining (editors), Data Mining and Knowledge Discovery, 17(2).
  4. 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.
  5. 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.
  6. Gary M. Weiss and Ye Tian (2006). Maximizing Classifier Utility when Training Data is Costly, ACM SIGKDD Explorations, 8(2):31-38.
  7. 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.
  8. Bianca Zadrozny, Gary. M. Weiss and Maytal Saar-Tsechansky (2006). UBDM 2006: Utility-Based Data Mining 2006 Workshop Report. ACM SIGKDD Explorations, 8(2), ACM Press, December 2006.
  9. 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.
  10. Gary. M. Weiss, Maytal Saar-Tsechansky and Bianca Zadrozny (2005). Report on UBDM-05: Workshop on Utility-Based Data Mining. ACM SIGKDD Explorations, 7(2):145-147, ACM Press, December 2005.
  11. 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.
  12. 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.
  13. 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.
  14. 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.   332 citations (189 citations to the journal version and 143 citations to the technical report version)

Rarity and Small Disjuncts

  1. Gary M. Weiss (2010). The Impact of Small Disjuncts on Classifier Learning. Annals of Information Systems, 8:193-226.
  2. Ye Tian, Gary M. Weiss, D. Frank Hsu and Qiang Ma (2009). A Combinatorial Fusion Method for Feature Construction, Proceedings of the 2009 International Conference on Data Mining, CSREA Press, 260-266.
  3. 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.
  4. Gary M. Weiss (2004). Mining with Rarity: A Unifying Framework, ACM SIGKDD Explorations, 6(1):7-19, June 2004.   208 citations
  5. 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.   44 citations
  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). 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.   34 citations
  8. 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.   122 citations
  9. 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. 26 citations
  10. 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.   41 citations

Event Prediction/Data Streams

  1. 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, Volume 3, 1248-1256. Copyright 2008, IGI Global, www.igi-global.com. Posted by permission of the publisher.
  2. 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.   32 citations
  3. 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.
  4. 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.
  5. 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.  113 citations
  6. 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.
  7. 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.

Telecommunications

  1. 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, Volume 1, 486-491. Copyright 2008, IGI Global, www.igi-global.com. Posted by permission of the publisher.
  2. 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.
  3. 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.
  4. 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.
  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. 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.

General Data Mining

  1. Gary M. Weiss and Brian Davison (2010). Data Mining. In H. Bidgoli (ed.), Handbook of Technology Management, Volume 2, John Wiley and Sons. Expected January, 2010.
  2. Gary M. Weiss (2009). Data Mining in the Real World: Experiences, Challenges, and Recommendations, Proceedings of the 2009 International Conference on Data Mining, CSREA Press, 124-130.
  3. Robert Stahlbock, Sven Crone, Stefan Lessmann, editors, Mahmoud Abou-Nasr, Hamid Arabnia, Philippe Lenca, Yanjun Li, Wolfram-M. Lippe, Anthony Scime, Gary M. Weiss assoc. editors (2009). Proceedings of the 2009 International Conference on Data Mining (DMIN '09), CSREA Press, Las Vegas, NV, July 2009.
  4. Samuel Moore, Daniel D'Addario, James Kurinskas, and Gary M. Weiss (2009). Are Decision Trees Always Greener on the Open (Source) Side of the Fence?, Proceedings of the 2009 International Conference on Data Mining, CSREA Press, 185-188.
  5. Robert Stahlbock, Sven Crone, Stefan Lessmann, editors, Hamid Arabnia, Philippe Lenca, Wolfram-M. Lippe, Gary M. Weiss assoc. editors (2008). Proceedings of the 2008 International Conference on Data Mining (DMIN '08), Volumes I & II. CSREA Press, Las Vegas, NV, July 2008.

Expert System and Object Technology

  1. 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.
  2. 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.
  3. 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.
  4. 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.

Feature Construction/Information Fusion

  1. Ye Tian, Gary M. Weiss, D. Frank Hsu and Qiang Ma (2009). A Combinatorial Fusion Method for Feature Construction, Proceedings of the 2009 International Conference on Data Mining, CSREA Press.
  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. 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, 16-23.

Semi-supervised Learning

  1. Jack Chongjie Xue and Gary M. Weiss (2009). Quantification and Semi-Supervised Classification Methods for Handling Changes in Class Distribution, Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-09), ACM Press, 897-905.
  2. 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, 16-23.

Link/Web Mining

  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, 16-23.

Genetic Algorithms

  1. 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. 32 citations
 
   
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