1. Home ›
  2. Centers and Labs
    3. ›
  3. DIAL Publications

DIAL Publications

2015

  1. Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson, and Gianluca Bontempi. “Calibrating Probability with Undersampling for Unbalanced Classification.” Proceedings of the 6th IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 159–166, 2015. PDF
  2. Yang Yang, Ryan N. Lichtenwalter, and Nitesh V. Chawla. “Evaluating Link Prediction Methods.” Knowledge and Information Systems (KAIS), 45(3):751–782, 2015. PDF
  3. Chao Huang, Dong Wang, and Nitesh V. Chawla. “Towards Time-Sensitive Truth Discovery in Social Sensing Applications.” Proceedings of the 12th IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 154–162, 2015. PDF
  4. Everaldo Aguiar, Saurabh Nagrecha, and Nitesh V. Chawla. “Predicting Online Video Engagement Using Clickstreams.” Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10, 2015. arXiv PDF
  5. Keith Feldman, Darcy A. Davis, and Nitesh V. Chawla. “Scaling and Contextualizing Personalized Healthcare: A Case Study of Disease Prediction Algorithm Integration.” Journal of Biomedical Informatics (JBI), vol. 57, pp. 377–385, 2015. PDF
  6. Reid A. Johnson, Ruobin Gong, Siobhan Greatorex-Voith, Anushka Anand, and Alan Fritzler. “A Data-Driven Framework for Identifying High School Students at Risk of Not Graduating on Time.” Bloomberg Data for Good Exchange. 2015. PDF
  7. Yuxiao Dong, Nitesh V. Chawla, Jie Tang, and Yang Yang. “The Evolution of Social Relationships and Strategies Across the Lifespan.” Proceedings of the 26th European Conference on Machine Learning and the 19th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 245–249, 2015. PDF
  8. Yuxiao Dong, Fabio Pinelli, Yiannis Gkoufas, Zubair Nabi, Francesco Calabrese, and Nitesh V. Chawla. “Inferring Unusual Crowd Events from Mobile Phone Call Detail Records.” Proceedings of the 26th European Conference on Machine Learning and the 19th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 474–492, 2015. arXiv PDF
  9. Sibel B. Kusimba, Yang Yang, and Nitesh V. Chawla. “Family Networks of Mobile Money in Kenya.” Information Technologies & International Development (ITID), 11(3):1–21, 2015. PDF
  10. Yuxiao Dong, Reid A. Johnson, Yang Yang, and Nitesh V. Chawla. “Collaboration Signatures Reveal Scientific Impact.” Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 480–487, 2015. PDF
  11. Saurabh Nagrecha, Nitesh V. Chawla, and Horst Bunke. “Recurrent Subgraph Prediction.” Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 416–423, 2015. PDF
  12. Yuxiao Dong, Jing Zhang, Jie Tang, Nitesh V. Chawla, and Bai Wang. “CoupledLP: Link Prediction in Coupled Networks.” Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 199–208, 2015. PDF
  13. Himabindu Lakkaraju, Everaldo Aguiar, Carl Shan, David I. Miller, Rayid Ghani, and Kecia L. Addison. “A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes.” Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1908–1918, 2015. PDF
  14. Keith Feldman and Nitesh V. Chawla. “Does Medical School Training Relate to Practice? Evidence from Big Data.” Big Data Journal, 3(2):103–113, 2015. PDF
  15. Reid A. Johnson, Troy Raeder, and Nitesh V. Chawla. “Optimizing Classifiers for Hypothetical Scenarios.” Proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 264–276, 2015. PDF
  16. Everaldo Aguiar, Himabindu Lakkaraju, Nasir Bhanpuri, David I. Miller, Ben Yuhas, and Kecia L. Addison. “Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time.” Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK), pp. 93–102, 2015. PDF
  17. Frederick Nwanganga, Everaldo Aguiar, G. Alex Ambrose, Victoria E. Goodrich, and Nitesh V. Chawla. “Qualitatively Exploring Electronic Portfolios: A Text Mining Approach to Measuring Student Emotion as an Early Warning Indicator.” Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK), pp. 422–423, 2015. PDF
  18. Yuxiao Dong, Jie Tang, and Nitesh V. Chawla. “Inferring Social Status and Rich Club Effects in Enterprise Communication Networks.” PLoS one, 10(3):e0119446, 2015. arXiv PDF
  19. Yuxiao Dong, Reid A. Johnson, and Nitesh V. Chawla. “Will This Paper Increase Your h-Index? Scientific Impact Prediction.” Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM), pp. 149–158, 2015. arXivPDF
  20. Yang Yang, Jie Tang, Yuxiao Dong, Qiaozhu Mei, Reid A. Johnson, and Nitesh V. Chawla. “Modeling the Interplay Between Individual Behavior and Network Distributions.” arXiv

2014

  1. Everaldo Aguiar, G. Alex Ambrose, Nitesh V. Chawla, Victoria E. Goodrich, and Jay B. Brockman. “Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence.” Journal of Learning Analytics, 1(3):7–33, 2014. PDF
  2. Yang Yang, Yuxiao Dong, and Nitesh V. Chawla. “Predicting Node Degree Centrality with Node Prominence Profile.” Scientific Reports, 4:7236, 2014. PDF SUPP
  3. Keith Feldman and Nitesh V. Chawla. “Admission Duration Model for Infant Treatment (ADMIT).” Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 583–587, 2014. PDF
  4. Yuxiao Dong, Yang Yang, Jie Tang, Yang Yang, and Nitesh V. Chawla. “Inferring User Demographics and Social Strategies in Mobile Social Networks.” Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 15–24, 2014. PDF
  5. Jian Xu, Thanuka L. Wickramarathne, Nitesh V. Chawla, Erin K. Grey, Karsten Steinhaeuser, Reuben P. Keller, John M. Drake, and David M. Lodge. “Improving Management of Aquatic Invasions by Integrating Shipping Network, Ecological and Environmental Data: Data Mining for Social Good.” Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1699–1708, 2014. PDF
  6. Thanuka L. Wickramarathne, Kamal Premaratne, Manohar Murthi, and Nitesh V. Chawla. “Convergence Analysis of Iterated Belief Revision in Complex Fusion Environments.” IEEE Journal of Selected Topics in Signal Processing (J-STSP), 8(4):598–612, 2014. PDF
  7. Andrew K. Rider, Tijana Milenković, Geoffrey H. Siwo, Richard S. Pinapati, Scott J. Emrich, Michael T. Ferdig, and Nitesh V. Chawla. “Networks’ Characteristics are Important for Systems Biology.” Network Science, 2(02):139–161, 2014. PDF SUPP
  8. Dipanwita Dasgupta and Nitesh V. Chawla. “Disease and Medication Networks: An Insight into Disease-Drug Interactions.” Proceedings of the 2nd International Conference on Big Data Analytics in Healthcare (BDAH), 2014. PDF
  9. Keith Feldman and Nitesh V. Chawla. “Scaling Personalized Healthcare with Big Data.” Proceedings of the 2nd International Conference on Big Data Analytics in Healthcare (BDAH), 2014. PDF
  10. Andrea Dal Pozzolo, Reid A. Johnson, Olivier Caelen, Serge Waterschoot, Nitesh V. Chawla, and Gianluca Bontempi. “Using HDDT to Avoid Instances Propagation in Unbalanced and Evolving Data Streams.” Proceedings of the 24th International Joint Conference on Neural Networks (IJCNN), pp. 588–594, 2014. PDF
  11. Victoria E. Goodrich, Everaldo Aguiar, G. Alex Ambrose, Leo H. McWilliams, Jay B. Brockman, and Nitesh V. Chawla. “Integration of ePortfolios in a First-Year Engineering Course for Measuring Student Engagement.” Proceedings of the American Society for Engineering Education Annual Conference (ASEE), pp. 24.785.1–24.785.16, 2014. PDF
  12. Dipanwita Dasgupta, Keith Feldman, Disha Waghray, W. A. Mikels-Carrasco, Patty Willaert, Debra A. Raybold, and Nitesh V. Chawla. “An Integrated and Digitized Care Framework for Successful Aging.” Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 440–443, 2014. PDF
  13. Everaldo Aguiar, Nitesh V. Chawla, Jay B. Brockman, G. Alex Ambrose, and Victoria E. Goodrich. “Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Retention.” Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK), pp. 103–112, 2014. PDF
  14. Ryan N. Lichtenwalter and Nitesh V. Chawla. “Vertex Collocation Profiles: Theory, Computation, and Results.” SpringerPlus, 3(1):116, 2014. PDF
  15. Andrew K. Rider, Geoffrey H. Siwo, Scott J. Emrich, Michael T. Ferdig, and Nitesh V. Chawla. “A Supervised Learning Approach to the Ensemble Clustering of Genes.” International Journal of Data Mining and Bioinformatics (IJDMB), 9(2):199–219, 2014. PDF
Pages: 1 2 3 4 5 6 7 8 9 10 11