Gary M. Weiss' Picture

 

 

View Gary Weiss's profile on LinkedIn ORCID logo

Publications (by Research Area)

You may also view the publications by: type, year, or top-10 most popular. Click on the title to view the publication in pdf format. The top-10 most popular papers are tagged (in red) with the approximate number of citations and clicking on the tag will bring up the list of citing papers in google scholar. You can also view my Google Scholar Profile.

INDEX OF RESEARCH AREAS
General Data Mining (21) Expert Systems and Object Technology (4)
Wireless Sensor Data Mining (18) Feature Construction/Information Fusion (3)
Utility-Based Data Mining (17) Medical/Bioinformatics (3)
Educational Data Mining (17) Semi-Supervised Learning (2)
Rarity and Small Disjuncts (12) Link/Web Mining (2)
Activity Recognition (12) Discrete Mathematics (1)
Event Prediction/Data Streams (8) Genetic Algorithms (1)
Biometrics (7) Education (1)
Telecommunications (6)  

General Data Mining

  1. Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, Cheng-Ying Yang, Hamid R. Arabnia, Leonidas Deligiannidis, editors, 2021. Advances in Data Science and Information Engineering: Proceedings from ICDATA 2020 and IKE 2020. Advances in Data Science and Information Engineering, Springer, Cham.
  2. Movses Musaelian, Md Zakirul Alam Bhuiyan, Gary M. Weiss, Tian Wang, Aliuz Zaman, and Thaier Hayajneh (2019). Data Science and Security in Digital Governance Aspects and an Elastic Bus Transportation Scheme, Proceedings of the 15th International Conference on Data Science, 84-89, Las Vegas, NV.
  3. Robert Stahlbock, Gary M. Weiss, and Mahmoud Abou-Nasr, editors, 2019. Proceedings of the 20l9 International Conference on Data Science (ICDATA '19), CSREA Press, Las Vegas, NV, July 2019.
  4. Xian Lai, and Gary M. Weiss (2018). RNN as a Multivariate Arrival Process Model: Modeling and Predicting Taxi Trips, Proceedings of the 14th International Conference on Data Science, Las Vegas, NV, 105-111.
  5. Emi N. Harry, and Gary M. Weiss (2018). Assessment of Minorities Access to Finance, Proceedings of the 14th International Conference on Data Science, Las Vegas, NV, 123-129.
  6. Robert Stahlbock, Gary M. Weiss, and Mahmoud Abou-Nasr, editors (2018). Proceedings of the 20l8 International Conference on Data Science (ICDATA '18), CSREA Press, Las Vegas, NV, July 2018.
  7. Robert Stahlbock, Mahmoud Abou-Nasr, and Gary M. Weiss, editors (2017). Proceedings of the 20l7 International Conference on Data Mining (DMIN '17), CSREA Press, Las Vegas, NV, July 2017.
  8. Robert Stahlbock and Gary M. Weiss, editors (2016). Proceedings of the 20l6 International Conference on Data Mining (DMIN '16), CSREA Press, Las Vegas, NV, July 2016.
  9. Robert Stahlbock and Gary M. Weiss, editors (2015). Proceedings of the 20l5 International Conference on Data Mining (DMIN '15), CSREA Press, Las Vegas, NV, July 2015.
  10. Mahmoud Abou-Nasr, Stefan Lessman, Robert Stahlbock, and Gary M. Weiss, editors (2015). Real World Data Mining Applications. Annals of Information Systems, Vol. 17, Springer. Available from Springer.
  11. Mahmoud Abou-Nasr, Stefan Lessman, Robert Stahlbock, and Gary M. Weiss (2015). Introduction. In Real World Data Mining Applications (special issue of Annals of Information Systems, Vol. 17), Springer, 1-14.
  12. Gary M. Weiss and Alexander Battistin (2014). Generating Well-Behaved Learning Curves: An Empirical Study, Proceedings of the Tenth International Conference on Data Mining, Las Vegas, NV, 210-213.
  13. Robert Stahlbock and Gary M. Weiss, editors (2014). Proceedings of the 20l4 International Conference on Data Mining (DMIN '14), CSREA Press, Las Vegas, NV, July 2014.
  14. Robert Stahlbock and Gary M. Weiss, editors (2013). Proceedings of the 2013 International Conference on Data Mining (DMIN '13), CSREA Press, Las Vegas, NV, July 2013.
  15. Robert Stahlbock and Gary M. Weiss, editors (2012). Proceedings of the 2012 International Conference on Data Mining (DMIN '12), CSREA Press, Las Vegas, NV, July 2012.
  16. Robert Stahlbock editor, Mahmoud Abou-Nasr, Hamid Arabnia, Nikolas Kourentzes, Philippe Lenca, Wolfram-M. Lippe, Gary M. Weiss assoc. editors (2011). Proceedings of the 2011 International Conference on Data Mining (DMIN '11), CSREA Press, Las Vegas, NV, July 2011. (TOC)
  17. Gary M. Weiss and Brian Davison (2010). Data Mining. In H. Bidgoli (ed.), Handbook of Technology Management, John Wiley and Sons, Volume 2, 542-555.
  18. Robert Stahlbock, Sven Crone editors, Mahmoud Abou-Nasr, Hamid Arabnia, Nikolas Kourentzes, Philippe Lenca, Wolfram-M. Lippe, Gary M. Weiss assoc. editors (2010). Proceedings of the 2010 International Conference on Data Mining (DMIN '10), CSREA Press, Las Vegas, NV, July 2010.
  19. 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.
  20. 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.
  21. 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.
  22. 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.

Wireless Sensor Data Mining Mining

  1. Kenichi Yoneda and Gary M. Weiss (2017). Mobile Sensor-based Biometrics using Common Daily Activities, Proceedings of the 8th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, New York, NY, 584-590.
  2. Francesco Ciuffo and Gary M. Weiss (2017). Smartwatch-Based Transcription Biometrics, Proceedings of the 8th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, New York, NY, 145-149.
  3. Gary M. Weiss, Jeffrey W. Lockhart, Tony T. Pulickal, Paul T. McHugh, Isaac H. Ronan, and Jessica L. Timko. Actitracker: A Smartphone-based Activity Recognition System for Improving Health and Well-Being, Proceedings of the IEEE 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, Canada.
  4. Gary M. Weiss, Jessica L. Timko, Catherine M. Gallagher, Kenichi Yoneda, and Andrew J. Schreiber (2016). Smartwatch-based Activity Recognition: A Machine Learning Approach, Proceedings of the 2016 IEEE International Conference on Biomedical and Health Informatics (BHI 2016), Las Vegas, NV, 426-429. 220 citations
  5. Andrew H. Johnston and Gary M. Weiss (2015). Smartwatch-Based Gait Recognition, Proceedings of the Seventh IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2015), Washington DC.
  6. Jeffrey W. Lockhart and Gary M. Weiss (2014). Limitations with Activity Recognition Methodolgy & Data Sets, Proceedings of the 2014 ACM Conference on Ubiquitous Computing Adjunct Publication (2nd International Workshop on Human Activity Sensing Corpus and its Application), Seattle, WA, 747-756.
  7. Jeffrey W. Lockhart and Gary M. Weiss (2014). The Benefits of Personalized Models for Smartphone-Based Activity Recognition, Proceedings of the 2014 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Philadelphia, PA, 614-622.
  8. Gary M. Weiss (2013). Smartphone Sensor Mining Research: Successes and Lessons, CUR Quarterly, 34(2):17-21.
  9. Gary M. Weiss, Ashwin Nathan, JB Kropp, and Jeffrey W. Lockhart (2013). WagTag™: A Dog Collar Accessory for Monitoring Canine Activity Levels, Proceedings of the ACM UbiComp International Atelier on Smart Garments and Accessories, ACM Press, Zurich, Switzerland, 405-413.
  10. Gary M. Weiss (2013). Your Smartphone Knows You Better Than You Know Yourself. Inside Science, January 4, 2013.
  11. Gary M. Weiss and Jeffrey W. Lockhart (2012). A Comparison of Alternative Client/Server Architectures for Ubiquitous Mobile Sensor-Based Applications, Proceedings of the ACM UbiComp 1st International Workshop on Ubiquitous Mobile Instrumentation, Pittsburgh, PA.
  12. Jeffrey W. Lockhart, Tony Pulickal, and Gary M. Weiss (2012). Applications of Mobile Activity Recognition, Proceedings of the ACM UbiComp International Workshop on Situation, Activity, and Goal Awareness, Pittsburgh, PA.
  13. Gary M. Weiss and Jeffrey W. Lockhart (2012). The Impact of Personalization on Smartphone-Based Activity Recognition, Papers from the AAAI-12 Workshop on Activity Context Representation: Techniques and Languages, AAAI Technical Report WS-12-05, Toronto, Canada, 98-104. 233 citations
  14. Gary M. Weiss and Jeffrey W. Lockhart (2011). Identifying User Traits by Mining Smart Phone Accelerometer Data, Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data (at KDD-11), San Diego, CA, 61-69.
  15. Jeffrey W. Lockhart, Gary M. Weiss, Jack C. Xue, Shaun T. Gallagher, Andrew B. Grosner, and Tony T. Pulickal (2011) Design Considerations for the WISDM Smart Phone-Based Sensor Mining Architecture, Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data (at KDD-11), San Diego, CA, 25-33.
  16. Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore (2010). Cell Phone-Based Biometric Identification, Proceedings of the IEEE Fourth International Conference on Biometrics: Theory, Applications and Systems (BTAS-10), Washington DC. 384 citations
  17. Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore. Activity Recognition using Cell Phone Accelerometers, ACM SIGKDD Explorations, 12(2):74-82. 3031 citations
  18. Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore (2010). Activity Recognition using Cell Phone Accelerometers, Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data (at KDD-10), Washington DC, 10-18.

Utility-Based Data Mining

  1. Ray M. Tischio, and Gary M. Weiss (2019). Identifying Classification Algorithms Most Suitable for Imbalanced Data, Proceedings of the 15th International Conference on Data Science, 106-111, Las Vegas, NV.
  2. Luís Torgo, Stan Matwin, Gary M. Weiss, Nuno Moniz, Paula Branco, editors (2018). Proceedings of The International Workshop on Cost-Sensitive Learning, published as Proceedings of Machine Learning Research (PMLR): Vol. 88, May 5, 2018.
  3. Gary M. Weiss and Alexander Battistin (2014). Generating Well-Behaved Learning Curves: An Empirical Study, Proceedings of the Tenth International Conference on Data Mining, Las Vegas, NV, 210-213.
  4. 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.
  5. 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)
  6. 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).
  7. 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.
  8. 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. 365 citations
  9. Gary M. Weiss and Ye Tian (2006). Maximizing Classifier Utility when Training Data is Costly, ACM SIGKDD Explorations, 8(2):31-38.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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. 227 citations
  17. 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.  1195 citations

Educational Data Mining

  1. Yijun Zhao, Alexander Borelli, A., Fernando Martinez, and Gary M. Weiss (2024). Admissions in the age of AI: detecting AI-generated application materials in higher education. Scientific Reports, 14:26411, Nov. 2024. https://doi.org/10.1038/s41598-024-77847-z
  2. Yijun Zhao, Zhengxin Qi, Son Tung Do, John Grossi, Jee Hun Kang, and Gary M. Weiss (2024). Predicting GRE Scores from Application Materials in Test-Optional Admissions. Proceedings of The 17th International Conference on Educational Data Mining (EDM24), International Educational Data Mining Society, Atlanta, Georgia, USA, July 14-17, 30-39.
  3. Fernando Martinez, Gary M. Weiss, Miguel Palma, Haoran Xue, Alexander Borelli, Yijun Zhao (2024). GPT vs. Llama2: Which Comes Closer to Human Writing?. Proceedings of The 17th International Conference on Educational Data Mining (EDM24) International Educational Data Mining Society, Atlanta, Georgia, USA, July 14-17, 107-116.
  4. Hyun Jeong, Gary M. Weiss, Audrey Leung, Daniel D. Leeds (2024). The Construction and Analysis of Course Grades Across Public Universities. Proceedings of The 17th International Conference on Educational Data Mining (EDM24), International Educational Data Mining Society, Atlanta, Georgia, USA, July 14-17, 643-648.
  5. Yijun Zhao, Tianyu Wang, Douglas Mensah, Ellise Parnoff, Siyi He, and Gary M. Weiss (2024). A Quantitative Machine Learning Approach to Evaluating Letters of Recommendation. Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS), Hawaii, USA, January 3-6, 1276-1284.
  6. Yijun Zhao, Zhengxin Qi, John Grossi, and Gary M. Weiss (2023). Gender and Culture Bias in Letters of Recommendation for Computer Science and Data Science Masters Programs, Scientific Reports, 13:14367, Sept 2023. https://doi.org/10.1038/s41598-023-41564-w
  7. Yijun Zhao, Xiaoyu Chen, Haoran Xue, and Gary M. Weiss (2023). A machine learning approach to graduate admissions and the role of letters of recommendation. PLoS ONE, 18:10:e0291107. https://doi.org/10.1371/journal.pone.0291107
  8. Gary M. Weiss, Luisa A. L. Rosa, Hyun Jeong and Daniel D. Leeds (2023). An Analysis of Grading Patterns in Undergraduate University Courses. Proceedings of the 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), IEEE, Torino, Italy, June 26-30, 310-315 (official version).
  9. Gary M. Weiss, Erik Brown, Michael Riad-Zaky, Ruby Iannone, and Daniel D. Leeds (2022). Assessing Instructor Effectiveness Based on Future Student Performance. Proceedings of The 15th International Conference on Educational Data Mining (EDM22), International Educational Data Mining Society, Durham, UK, July 24-27, 616-620.
  10. Gary M. Weiss, Joseph Denham, and Daniel D. Leeds (2022). The Impact of Semester Gaps on Student Grades. Proceedings of The 15th International Conference on Educational Data Mining (EDM22), International Educational Data Mining Society, Durham, UK, July 24-27, 612-615.
  11. Daniel D. Leeds, Cody Chen, Yijun Zhao, Fiza Metla, James Guest, and Gary M. Weiss (2022). Generalized Sequential Pattern Mining of Undergraduate Courses. Proceedings of The 15th International Conference on Educational Data Mining (EDM22), International Educational Data Mining Society, Durham, UK, July 24-27, 629-633.
  12. Faiza Khan, Gary M. Weiss, and Daniel D. Leeds (2021). Predicting the Academic Performance of Undergraduate Computer Science Students Using Data Mining. In: Arabnia H.R., Deligiannidis L., Tinetti F.G., Tran QN. (eds) Advances in Software Engineering, Education, and e-Learning. Transactions on Computational Science and Computational Intelligence. Springer, Cham, 303-317.
  13. Gary M. Weiss, Nam Nguyen, Karla Dominguez and Daniel D. Leeds (2021). Identifying Hubs in Undergraduate Course Networks Based on Scaled Co-Enrollments. Proceedings of The 14th International Conference on Educational Data Mining (EDM21), International Educational Data Mining Society, Paris, France, 809-813.
  14. Tess Gutenbrunner, Daniel D. Leeds, Spencer Ross, Michael Riad-Zaky, and Gary M. Weiss (2021). Measuring the Academic Impact of Course Sequencing using Student Grade Data. Proceedings of The 14th International Conference on Educational Data Mining (EDM21), International Educational Data Mining Society, Paris, France, 799-803.
  15. Daniel D. Leeds, Tianyi Zhang and Gary M. Weiss (2021). Mining Course Groupings using Academic Performance. Proceedings of The 14th International Conference on Educational Data Mining (EDM21), International Educational Data Mining Society, Paris, France, 804-808.
  16. Samuel A. Stein, Gary M. Weiss, Yiwen Chen, and Daniel D. Leeds (2020). A College Major Recommendation System. Proceedings of the Fourteenth ACM Conference on Recommender Systems (RECSYS 20), 640-644, September 2020.
  17. Yijun Zhao, Qiangwen Xu, Ming Chen, and Gary M. Weiss (2020). Predicting Student Performance in a Master of Data Science Program using Admissions Data. Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020), 325-333.

Rarity and Small Disjuncts

  1. Ray M. Tischio, and Gary M. Weiss (2019). Identifying Classification Algorithms Most Suitable for Imbalanced Data, Proceedings of the 15th International Conference on Data Science, 106-111, Las Vegas, NV.
  2. Gary M. Weiss (2013). Foundations of Imbalanced Learning (preprint). In H. He and Y. Ma (eds.), Imbalanced Learning: Foundations, Algorithms and Applications, Wiley-IEEE Press, 13-41. Preprint posted by permission of publisher. Book available for purchase [Amazon, Wiley].
  3. Gary M. Weiss (2010). The Impact of Small Disjuncts on Classifier Learning. Annals of Information Systems, 8:193-226.
  4. 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.
  5. Gary M. Weiss (2005). Mining with 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, 747-757.
  6. Gary M. Weiss (2004). Mining with Rarity: A Unifying Framework, ACM SIGKDD Explorations, 6(1):7-19, June 2004.   1195 citations
  7. 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. 
  8. 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.
  9. 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.
  10. 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.   377 citations
  11. 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.
  12. 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.  

Activity Recognition

  1. Gary M. Weiss, Jeffrey W. Lockhart, Tony T. Pulickal, Paul T. McHugh, Isaac H. Ronan, and Jessica L. Timko (2016). Actitracker: A Smartphone-based Activity Recognition System for Improving Health and Well-Being, Proceedings of the IEEE 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, Canada.
  2. Gary M. Weiss, Jessica L. Timko, Catherine M. Gallagher, Kenichi Yoneda, and Andrew J. Schreiber (2016). Smartwatch-based Activity Recognition: A Machine Learning Approach, Proceedings of the 2016 IEEE International Conference on Biomedical and Health Informatics (BHI 2016), Las Vegas, NV, 426-429. 220 citations
  3. Jeffrey W. Lockhart and Gary M. Weiss (2014). Limitations with Activity Recognition Methodolgy & Data Sets, Proceedings of the 2014 ACM Conference on Ubiquitous Computing Adjunct Publication (2nd International Workshop on Human Activity Sensing Corpus and its Application), Seattle, WA, 747-756.
  4. Jeffrey W. Lockhart and Gary M. Weiss (2014). The Benefits of Personalized Models for Smartphone-Based Activity Recognition, Proceedings of the 2014 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Philadelphia, PA, 614-622.
  5. Gary M. Weiss (2013). Smartphone Sensor Mining Research: Successes and Lessons, CUR Quarterly, 34(2):17-21.
  6. Gary M. Weiss, Ashwin Nathan, JB Kropp, and Jeffrey W. Lockhart (2013). WagTag™: A Dog Collar Accessory for Monitoring Canine Activity Levels, Proceedings of the ACM UbiComp International Atelier on Smart Garments and Accessories, ACM Press, Zurich, Switzerland, 405-413.
  7. Gary M. Weiss (2013). Your Smartphone Knows You Better Than You Know Yourself. Inside Science, January 4, 2013.
  8. Gary M. Weiss and Jeffrey W. Lockhart (2012). A Comparison of Alternative Client/Server Architectures for Ubiquitous Mobile Sensor-Based Applications, Proceedings of the ACM UbiComp 1st International Workshop on Ubiquitous Mobile Instrumentation, Pittsburgh, PA.
  9. Jeffrey W. Lockhart, Tony Pulickal, and Gary M. Weiss (2012). Applications of Mobile Activity Recognition, Proceedings of the ACM UbiComp International Workshop on Situation, Activity, and Goal Awareness, Pittsburgh, PA.
  10. Gary M. Weiss and Jeffrey W. Lockhart (2012). The Impact of Personalization on Smartphone-Based Activity Recognition, Papers from the AAAI-12 Workshop on Activity Context Representation: Techniques and Languages, AAAI Technical Report WS-12-05, Toronto, Canada, 98-104. 233 citations
  11. Jeffrey W. Lockhart, Gary M. Weiss, Jack C. Xue, Shaun T. Gallagher, Andrew B. Grosner, and Tony T. Pulickal (2011) Design Considerations for the WISDM Smart Phone-Based Sensor Mining Architecture, Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data (at KDD-11), San Diego, CA, 25-33.
  12. Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore (2010). Activity Recognition using Cell Phone Accelerometers, ACM SIGKDD Explorations, 12(2):74-82. 2371 citations

Event Prediction/Data Streams

  1. Md Zakirul Alam Bhuiyan, Jie Wu, Gary M. Weiss, Thaier Hayajneh, Tian Wang, and Guojun Wang (2017). Event Detection through Differential Pattern Mining in Cyber-Physical Systems, IEEE Transactions on Big Data.
  2. 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.
  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 (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.
  5. 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.
  6. 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.  377 citations
  7. 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.
  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.

Biometrics

  1. Gary M. Weiss, Kenichi Yoneda, and Thaier Hayajneh (2019). Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living. IEEE Access, 7:133190-133202, Sept. 2019. 205 citations
  2. Abdullah Alhayajneh, Alessandro N. Baccarini, Gary M. Weiss, Thaier Hayajneh, and Aydin Farajidavar (2018). Biometric Authentication and Verification for Medical Cyber Physical Systems, Electronics, 7(12), 436.
  3. Kenichi Yoneda and Gary M. Weiss (2017). Mobile Sensor-based Biometrics using Common Daily Activities, Proceedings of the 8th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, New York, NY, 584-590.
  4. Francesco Ciuffo and Gary M. Weiss (2017). Smartwatch-Based Transcription Biometrics, Proceedings of the 8th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, New York, NY.
  5. Andrew H. Johnston and Gary M. Weiss (2015). Smartwatch-Based Gait Recognition, Proceedings of the Seventh IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2015), Washington DC.
  6. Gary M. Weiss and Jeffrey W. Lockhart (2011). Identifying User Traits by Mining Smart Phone Accelerometer Data, Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data (at KDD-11), San Diego, CA, 61-69.
  7. Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore (2010). Cell Phone-Based Biometric Identification, Proceedings of the IEEE Fourth International Conference on Biometrics: Theory, Applications and Systems (BTAS-10), Washington DC. 384 citations

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.

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.

Medical/Bioinformatics

  1. Yuhan Hao, Gary M. Weiss, and Stuart M. Brown (2019). Identification of Candidate Genes Responsible for Age-Related Macular Degeneration Using Microarray Data, Biotechnology: Concepts, Methodologies, Tools, and Applications, Chapter 38, IGI Global, 969-1001. (reprint of previous work)
    doi:10.4018/978-1-5225-8903-7.ch038
  2. Yuhan Hao, Gary M. Weiss, Stuart Brown (2018). Identification of Candidate Genes Responsible for Age-related Macular Degeneration using Microarray Data, International Journal of Service Science, Management, Engineering, and Technology, 9(2):33-60.
  3. Yuhan Hao and Gary M. Weiss (2016). Gene Selection from Microarray Data for Age-related Macular Degeneration by Data Mining, Proceedings of the 2016 International Conference on Data Mining (DMIN 2016), Las Vegas, NV, 125 - 129.

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.
  2. Andrew H. Johnston, and Gary M. Weiss (2017). Identifying Sunni Extremist Propaganda with Deep Learning, Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, Honolulu, Hawaii.

Discrete Mathematics

  1. Damian M. Lyons, Christina Papadakis-Kanaris, Gary M. Weiss, Arthur G. Werschulz (2012). Fundamentals of Discrete Structures 2nd edition, Pearson Learning Solutions. Available at Amazon.

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.

Education

  1. Gary M. Weiss (2013). Smartphone Sensor Mining Research: Successes and Lessons, CUR Quarterly, 34(2):17-21.