2014
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Y. Dong, R. Johnson, and N. V. Chawla (Feb 2015)
Will This Paper Increase Your h-index? Scientific Impact Prediction
Proceedings of 7th International Conference on Web Science and Data Mining (ACM WSDM)
Y. Yang, Y. Dong, and N. V. Chawla(November 2014)
Predicting Node Degree Centrality with the Node Prominence Profile
Scientific Reports
K. Feldman and N. V. Chawla (November 2014)
Admission Duration Model for Infant Treatment (ADMIT)
Proceeding of IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)
Y. Yang, R. Lichtenwalter and N.V. Chawla (October 2014)
Evaluating link prediction methods)
Knowledge and Information Systems Journal
J Xu, TL Wickramarathne, NV Chawla, E Grey, K Steinhaeuser, RP Keller, JM Drake and DM Lodge (Aug'2014)
Improving Management of Aquatic Invasions by Integrating Shipping Network, Ecological and Environmental Data: Data Mining for Social Good
Proc. of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data mining (KDD)
Yuxiao Dong, Yang Yang, Yang Yang, Jie Tang, and Nitesh V. Chawla (Aug'2014)
Inferring User Demographics and Social Strategies in Mobile Social Networks
Proc. of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data mining (KDD)
Keith Feldman and Nitesh V. Chawla (July'2014)
Scaling Personalized Healthcare with Big Data
2nd International Conference on Big Data Analytics in Healthcare (BDAH'14)
Dipanwita Dasgupta and Nitesh V. Chawla (July'2014)
Disease and Medication Networks: An Insight into Disease-Drug Interactions
2nd International Conference on Big Data Analytics in Healthcare (BDAH'14)
Andrea Dal Pozzolo, Reid Johnson, Olivier Caelen, Serge Waterschoot, Nitesh V Chawla and Gianluca Bontempi (July 2014)
HDDT to avoid instances propagation in unbalanced and evolving data streams
IEEE IJCNN
TL Wickramarathne, Kamal Premaratne, Manohar Murthi and Nitesh V. Chawla (April 2014)
Convergence Analysis of Iterated Belief Revision in Complex Fusion Environments
IEEE Journal of Selected Topics in Signal Processing
Dipanwita Dasgupta, Keith Feldman, Disha Waghray, W.A. Mikels-Carrasco, Patty Willaert, Debra A. Raybold, and Nitesh V. Chawla (June 2014)
Integrated Care Framework for Successful Aging
IEEE-EMBS International Conferences on Biomedical and Health Informatics
Ryan Lichtenwalter and Nitesh V. Chawla (February 2014)
Vertex collocation profiles: theory, computation, and results
SpringerPlus 2014, 3:116
Andrew Rider, Tijana Milenkovic, Geoffrey Siwo, Andrew Pinapati, Scott Emrich, Michael Ferdig, Nitesh V. Chawla (2014)
Networks' characteristics are important for systems biology
Network Science
Everaldo Aguiar, Nitesh V. Chawla, Jay Brockman, G. Alex Ambrose, Victoria Goodrich, (March, 2014)
Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Retention
Fourth International Conference on Learning Analytics and Knowledge (LAK)
2013
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Siwo et al. (August 2013)
Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach
Genome Res. 2013. 23: 1928-1937
Saurav Pandit, Jonathan Koch, Yang Yang, Brian Uzzi and Nitesh V. Chawla (November 2013)
Red Black Network: Temporal and Topological Analysis of Two Intertwined Social Networks
32nd Annual Military Communications Conference, 2013 (MILCOM'13)
C. Wang, O. Lizard, D. Hachen, A. Strathman, Z. Toroczkai, N. V. Chawla (2013)
A dyadic reciprocity index for repeated interaction networks
Network Science
T. Ryan Hoens, Marina Blanton, Aaron Steele, Nitesh V. Chawla (September 2013)
Reliable medical recommendation systems with patient privacy
ACM Transactions on Intelligent Systems and Technology, Volume 4 Issue 4, September 2013
Andrew Rider, Nitesh V. Chawla, and Scott Emrich (2013)
A survey of current integrative network algorithms for systems biology
Systems Biology: Integrative Biology and Simulation Tools, 2013
Andrew Rider, Reid Johnson, Darcy Davis, T. Ryan Hoens and Nitesh V. Chawla (July 2013)
Classifier Evaluation With Missing Negative Class Labels
Twelth International Symposium on Intelligent Data Analysis, 2013 (IDA'13)
Andrew Rider and Nitesh V. Chawla (July 2013).
An Ensemble Topic Model for Sharing Healthcare Data and Predicting Disease Risk
ACM International Conference on Bioinformatics, Computational Biology, and Biomedical Informatics, 2013 (ACM BCB'13)
Yang Yang, Nitesh V. Chawla, Prithwish Basu, Bhaskar Prabhala, Thomas La Porta
Link Prediction in Human Mobility Networks
ACM/IEEE ASONAM'13
Yuxiao Dong, Jie Tang, Tiancheng Lou, Bin Wu, Nitesh V. Chawla (Sep 2013).
How Long will She Call Me? Distribution, Social Theory and Duration Prediction
ECML/PKDD' 13, Prague, Sep. 2013. (Acceptance rate: )
Nitesh V. Chawla and Darcy Davis (July 2013).
Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework.
Journal of General Internal Medicine (JGIM)
Nathan Regola, David Cieslak, Nitesh V. Chawla (2013).
The Need to Consider Hardware Selection when Designing Big Data Applications Supported by Metadata.
Big Data Management, Technologies, and Applications
Saurabh Nagrecha, Pawan J. Lingras, and Nitesh V. Chawla
Comparison of Gene Co-expression Networks and Bayesian Networks
5th Asian Conference on Intelligent Information and Database Systems (ACIIDS), Kuala Lumpur, Malaysia, March 18-20, 2013.
Rachael Purta, Saurabh Nagrecha, and Gregory Madey
Multi-hop Communications in a Swarm of UAVs
Agent-Directed Simulation (ADS) Symposium, San Diego, CA, April 7-10, 2013.
Nathan Regola and Nitesh V. Chawla (March 2013).
Storing and Using Health Data in a Virtual Private Cloud.
Journal of Medical Internet Research, 15(3):e63. (JMIR)
Robert Thompson and Nitesh V. Chawla
Addressing Challenges in Prescrpition Management
24th Annual Conference of the Production and Operations Management Society (POMS)
2012
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Yang Yang, Nitesh V. Chawla, Yizhou Sun, and Jiawei Han (December 2012).
Predicting links in multi-relational and heterogeneous networks
Proc. of the 12th IEEE International Conference on Data Mining (ICDM'12), Brussels, Belgium, Dec. 2012 (Acceptance rate: 10.7%)
Yang Yang, Nitesh V. Chawla, Xiaohui Lu, and Sibel Adali (May 2012).
Prominence in Networks: A Co-evolving process
IEEE 2nd International Network Science Workshop (NSW'12), West Point, NY, May. 2012 (Acceptance rate: N/A)
Mária Ercsey-Ravasz, Ryan N. Lichtenwalter, Nitesh V. Chawla, and Zoltán Toroczkai (2012)
Range-limited centrality measures in complex networks
Phys. Rev. E 85, 066103
T. Ryan Hoens,Marina Blanton, Aaron Steele, and Nitesh V. Chawla (September 2012. ACCEPTED)
Reliable Medical Recommendation Systems with Patient Privacy
ACM Transactions on Intelligent Systems and Technology (ACM TIST)
Yuxiao Dong, Jie Tang, Sen Wu, Jilei Tian, Nitesh V. Chawla, Jinghai Rao, and Huanhuan Cao (December 2012).
Link Prediction and Recommendation Across Heterogeneous Social Networks
Proc. of the 12th IEEE International Conference on Data Mining (ICDM'12), Brussels, Belgium, Dec. 2012 (Acceptance rate: 10.7%)
Nitesh V. Chawla and Yang Yang (December 2012).
Link Prediction: A Primer
in Reda Alhajj and Jon Rokne (eds.), Encyclopedia of Social Network Analysis and Mining by Springer, 2012.
Saurav Pandit, Yang Yang, and Nitesh V. Chawla (December 2012).
Maximizing Information Spread Through Influence Structures in Social Networks
DaMNet Workshop, in Proc. of the 12th IEEE International Conference on Data Mining (ICDM'12), Brussels, Belgium, Dec. 2012
Reid A. Johnson, Nitesh V. Chawla, and Jessica J. Hellmann (October 2012).
Species Distribution Modeling and Prediction: A Class Imbalance Problem
NASA Conference on Intelligent Data Understanding (CIDU), Boulder, CO.
Ryan Lichtenwalter and Nitesh V. Chawla (2012).
Link Prediction: Fair and Effective Evaluation
IEEE/ACM International Conference on Social Networks Analysis and Mining, 2012 (Acceptance rate: 16%)
Yang Yang, Yizhou Sun, Saurav Pandit, Nitesh V. Chawla and Jiawei Han (December 2012)
Perspective on Measurement Metrics for Community Detection Algorithms
in Zeki Erdem, Tansel Ozyer, Suheil Khoury, Jon Rokne(eds.), Studies in Mining Social Networks and Security Informatics by Springer, 2012.
T. Ryan Hoens and Nitesh V. Chawla (August 2012).
Learning in Non-stationary Environments with Class Imbalance
18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).
T. Ryan Hoens and Nitesh V. Chawla (August 2012).
Imbalanced Datasets: From Sampling to Classifiers
Book Chapter in, Imbalanced Learning: Foundations, Algorithms, and Applications
Nathan Regola, David A. Cieslak and Nitesh V. Chawla (June 2012).
The Constraints of Magnetic versus Flash Disk Capabilities in Big Data Analysis
Proc. of Second Workshop on Architectures and Systems for Big Data, ACM.
Darcy Davis, Ryan Lichtenwalter and Nitesh V. Chawla.
Supervised Methods for Multi-Relational Link Prediction
Social Networks Analysis and Mining
Andrew K. Rider, Geoffrey Siwo, Scott J. Emrich, Michael T. Ferdig, Nitesh V. Chawla (to appear).
A Supervised Learning Approach to the Ensemble Clustering of Genes
International Journal of Data Mining and Bioinformatics
T. Ryan Hoens, Robi Polikar, Nitesh V. Chawla (January 2012).
Learning from Streaming Data with Concept Drift and Imbalance: An Overview
Progress in Artificial Intelligence, Springer, 1(1).
Ryan N. Lichtenwalter and Nitesh V. Chawla.
Vertex Collocation Profiles: Subgraph Counting for Link Analysis and Prediction
Proc. of 21st International Confernence on World Wide Web (WWW) Conference (Acceptance rate: 12%)
Yizhou Sun, Jiawei Han, Charu C. Aggarwal, and Nitesh V. Chawla
When Will It Happen? Relationship Prediction in Heterogeneous Information Networks
Proc. 2012 ACM Int. Conf. on Web Search and Data Mining (WSDM'12), Seattle, WA, Feb. 2012. (Acceptance rate: 75/362 = 20.7%)
T. Ryan Hoens, Qi Aian, Nitesh V. Chawla, and Zhi-Hua Zhou
Building Decision Trees for the Multi-class Imbalance Problem
Proc. of 16th Pacific Asia Conference on Knowledge Discovery and Data Mining (Acceptance rate: 28%)
2011
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T. Ryan Hoens, Nitesh V. Chawla, and Robi Polikar (December 2011)
Heuristic Updatable Weighted Random Subspaces for Non-stationary Environments
IEEE International Conference on Data Mining (ICDM) (Acceptance rate: 12%)
Nathan Regola and Nitesh V. Chawla (December 2011).
Small Compute Clusters for Large-Scale Data Analysis
International Conference on Information Technology, Systems and Management, Kozhikode, Kerala, India.
Jake T. Lussier and Nitesh V. Chawla (September 2011).
Network Effects on Tweeting.
Discovery Science, Lecture Notes in Computer Science, Springer-Verlag, 6926(2011), 209-220, DOI: 10.1007/978-3-642-24477-3_18.
Ryan N. Lichtenwalter and Nitesh V. Chawla.
LPmade: Link Prediction Made Easy
Journal of Machine Learning Research.
Darcy Davis and Nitesh V. Chawla (July 2011).
Exploring and Exploiting Disease Interactions from Multi-Relational Gene and Phenotype Networks.
PLoS ONE, 6(7), e22670
Troy Raeder, Omar Lizardo, David Hachen, Nitesh V. Chawla (in press).
Predictors of short-term decay of cell phone contacts in a large-scale communication network.
Social Networks.
Jose G. Moreno-Torres, Troy Raeder, Rocio Alaiz-Rodriguez, Nitesh V. Chawla, Francisco Herrera (January 2012).
A Unifying View on Dataset Shift in Classification.
Pattern Recognition, 45(1), 521-530.
Karsten Steinhaeuser, Auroop R. Ganguly and Nitesh V. Chawla (June 2011).
Multivariate and Multiscale Dependence in the Global Climate System Revealed Through Complex Networks.
Climate Dynamics, doi:10.1007/s00382-011-1135-9.
David A. Cieslak, T. Ryan Hoens, Nitesh V. Chawla and W. Philip Kegelmeyer (June 2011).
Hellinger Distance Decision Trees are Robust and Skew-Insensitive
Data Mining and Knowledge Discovery (DMKD), doi:10.1007/s10618-011-0222-1.
Saurav Pandit, Yang Yang, Vikas Kawadia, Sameet Sreenivasan, and Nitesh V. Chawla (to appear).
Detecting Communities in Time-evolving Proximity Networks
IEEE Network Science Workshow (NSW), West Point, NY.
Karsten Steinhaeuser, Nitesh V. Chawla and Auroop R. Ganguly (to appear).
Comparing Predictive Power in Climate Data: Clustering Matters.
Symposium on Spatial and Temporal Databases (SSTD), Minneapolis, MN.
Ryan N. Lichtenwalter and Nitesh V. Chawla (July 2011).
Disnet: A Framework for Distributed Graph Computation.
The International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Kaohsiung, Taiwan.
Darcy Davis, Ryan Lichtenwalter, and Nitesh V. Chawla (July 2011).
Multi-Relational Link Prediction in Heterogeneous Information Networks.
The International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Kaohsiung, Taiwan.
Yang Yang, Yizhou Sun, Saurav Pandit, Nitesh Chawla, and Jiawei Han (July 2011).
Is Objective Function the Silver Bullet? A Case Study of Community Detection Algorithms on Social Networks.
The International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Kaohsiung, Taiwan.
Alex Pelan, Karsten Steinhaeuser, Nitesh V. Chawla, Dilkushi A. de Alwis Pitts and Auroop R. Ganguly (April 2011).
Empirical Comparison of Correlation Measures and Pruning Levels in Complex Networks Representing the Global Climate System.
IEEE Symposium Series on Computational Intelligence and Data Mining (CIDM), Paris, France.
2010
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Karsten Steinhaeuser, Nitesh V. Chawla, and Auroop R. Ganguly (to appear).
Complex Networks as a Unified Framework for Descriptive Analysis and Predictive Modeling in Climate Science.
Statistical Analysis and Data Mining, doi:0.1002/sam.10100.
Andrew K. Rider, Geoffrey Siwo, Nitesh V. Chawla, Michael Ferdig and Scott J. Emrich (to appear).
A Statistical Approach to Finding Overlooked Genetic Associations.
BMC Bioinformatics.
Troy Raeder, T. Ryan Hoens, and Nitesh V. Chawla (December 2010).
Consequences of Variability in Classifier Performance Estimates
IEEE International Conference on Data Mining (ICDM), Sydney, Australia.
Andrew K. Rider, Geoffrey Siwo, Nitesh V. Chawla, Michael Ferdig and Scott J. Emrich (December 2010).
A Supervised Learning Approach to the Unsupervised Clustering of Genes.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Hong Kong.
T. Ryan Hoens, Marina Blanton and Nitesh V. Chawla (November 2010).
Reliable Medical Recommendation Systems with Patient Privacy
1st ACM International Health Informatics Symposium (IHI), Washington D.C.
Karsten Steinhaeuser, Nitesh V. Chawla and Auroop R. Ganguly (October 2010).
Complex Networks in Climate Science: Progress, Opportunities and Challenges.
IEEE Conference on Intelligent Data Understanding (CIDU), Mountain View, CA.
Qi Liao, Aaron Striegel and Nitesh V. Chawla (September 2010).
Visualizing Graph Dynamics and Similarity for Enterprise Network Security and Management
ACM 7th International Symposium on Visualization for Cyber Security (VizSec).
Troy Raeder and Nitesh V. Chawla (August 2010).
Market Basket Analysis with Networks.
Social Networks Analysis and Modeling Journal.
T. Ryan Hoens, Marina Blanton and Nitesh V. Chawla (August 2010).
A Private and Reliable Recommendation System for Social Networks.
IEEE International Conference on Information Privacy, Security, Risk and Trust (PASSAT), Minneapolis, MN.
Gregory Ditzler, Nitesh V. Chawla and Robi Polikar (August 2010).
An Incremental Learning Algorithm for Nonstationary Environments and Class Imbalance.
International Conference on Pattern Recognition (ICPR), Istanbul, Turkey.
Ryan Lichtenwalter, Jake Lussier and Nitesh V. Chawla (July 2010).
New Perspectives and Methods in Link Prediction.
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
Karsten Steinhaeuser, Nitesh V. Chawla and Auroop R. Ganguly (July 2010).
An Exploration of Climate Data Using Complex Networks.
ACM SIGKDD Explorations, 12(1), 25-32.
Darcy Davis and Nitesh V. Chawla (July 2010).
Exploring Disease Interactions Using Combined Gene and Phenotype Networks.
International Conference on Intelligent Systems for Molecular Biology.
Karsten Steinhaeuser, Nitesh V. Chawla and Auroop R. Ganguly (June 2010).
Descriptive Analysis of the Global Climate System and Predictive Modeling for Uncertainty Reduction in Climate Projections using Complex Networks.
International Conference on Computational Sustainability (COMPSUST), Cambridge, MA.
James Gray, Darcy Davis, DeWayne Pursley, Jane Smallcomb, Alon Geva and Nitesh V. Chawla (June 2010).
Network Analysis of Team Structure in the Neonatal Intensive Care Unit.
Journal of the American Academy of Pediatrics, 125(6), 1460-1467.
T. Ryan Hoens and Nitesh V. Chawla (June 2010).
Generating Diverse Ensembles to Counter the Problem of Class Imbalance.
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Hyderabad, India.
Troy Raeder, Marina Blanton, Nitesh V. Chawla and Keith Frikken (June 2010).
Privacy-Preserving Network Aggregation.
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Hyderabad, India.
Darcy Davis, Nitesh V. Chawla, Nicholas A. Christakis and Albert László Barabási (May 2010).
Time to CARE: A Collaborative Filtering Engine for Practical Disease Prediction.
Data Mining and Knowledge Discovery, 20(3), 388-415.
Karsten Steinhaeuser and Nitesh V. Chawla (April 2010).
Identifying and Evaluating Community Structure in Complex Networks.
Pattern Recognition Letters, 31(5), 413-421.
Wei Liu, Sanjay Chawla, David A. Cieslak and Nitesh V. Chawla (April 2010).
A Robust Decision Tree Algorithm for Imbalanced Data Sets.
SIAM Conference on Data Mining (SDM), Columbus, OH.
Jake Lussier, Troy Raeder and Nitesh V. Chawla (March 2010).
User Generated Content Consumption and Social Networking in Knowledge-Sharing OSNs.
Social Computing, Behavioral Modeling, and Prediction, Springer, 209-216.
Lorenzo Beretta, Alessandro Santaniello, Francesca Cappiello, Nitesh V. Chawla, Madelon C. Vonk, Patricia E. Carreira, Yannick Allanore, Delia A. Popa-Diaconu, Marta Cossu, Francesca Bertolotti, Gianfranco Ferraccioli, Antonino Mazzone, and Rafaella Scorza (2010).
Development of a Five-Year Mortality Model in Systemic Sclerosis Patients by Different Analytical Approaches.
Clinical and Experimental Rheumatology, 28(2) Supplement 58, 18-27.
2009
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Ryan N. Lichtenwalter, Katerina Lichtenwalter and Nitesh V. Chawla (December 2009).
Applying Learning Algorithms to Music Generation.
Indian International Conference on Artificial Intelligence (IIJCAI), Tumkur, India.
Olufemi A. Omitaomu, Auroop R. Ganguly, João Gama, Ranga Raju Vatsavai, Nitesh V. Chawla and Mohamed M. Gaber (December 2009).
Knowledge Discovery from Sensor Data.
ACM SIGKDD Explorations, 11(2), 84-87.
Faruck Morcos, Charles Lamanna, Nitesh V. Chawla and Jesús Izaguirre (July 2009).
Determination of Specificity Residues in Two Component Systems using Graphlets.
International Conference on Bioinformatics & Computational Biology (BIOCOMP), Las Vegas, NV.
Troy Raeder and Nitesh V. Chawla (July 2009).
Model Monitor (M^2): Evaluating, Comparing, and Monitoring Models.
Journal of Machine Learning Research (JMLR), 10, 1387-1390.
Troy Raeder and Nitesh V. Chawla (July 2009).
Modeling a Store's Product Space as a Social Network.
ACM/IEEE Conference on Advances in Social Network Analysis and Mining (ASONAM), Athens, Greece.
Karsten Steinhaeuser, Nitesh V. Chawla and Auroop R. Ganguly (June 2009).
Discovery of Climate Patterns with Complex Networks.
International Workshop and Conference on Network Science (NetSci), Venice, Italy.
Karsten Steinhaeuser, Nitesh V. Chawla and Auroop R. Ganguly (June 2009).
An Exploration of Climate Data Using Complex Networks.
ACM SIGKDD Workshop on Knowledge Discovery from Sensor Data (SensorKDD), Paris, France.
Sean McRoskey, Jim Notwell, Nitesh V. Chawla and Christian Poellabauer (June 2009).
Mining in a Mobile Environment.
ACM SIGKDD Workshop on Knowledge Discovery from Sensor Data (SensorKDD), Paris, France.
Ryan Lichtenwalter and Nitesh V. Chawla (accepted April 2009, to appear).
Learning to Classify Data Streams with Imbalanced Class Distributions.
Proceedings of the PAKDD Conference, LNCS, Springer.
Ryan N. Lichtenwalter and Nitesh V. Chawla (April 2009).
Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams.
PAKDD Workshop for Data Mining When Classes are Imbalanced and Errors have Costs (ICEC), Bangkok, Thailand.
Laritza M. Taft, R. Scott Evans, Chi-Ren Shyu, Marlene J. Egger, Nitesh V. Chawla, Joyce A. Mitchell, Sidney N. Thornton, Bruce Bray and Michael W. Varner (April 2009).
Countering Imbalanced Datasets to Improve Adverse Drug Event Predictive Models in Labor and Delivery.
Journal of Biomedical Informatics (JBI), 42(2), 356-364.
Karsten Steinhaeuser and Nitesh V. Chawla (March 2009).
A Network-Based Approach to Understanding and Predicting Diseases.
Social Computing, Behavioral Modeling, and Prediction, Springer, 209-216.
David A. Cieslak and Nitesh V. Chawla (January 2009).
A Framework for Monitoring Classifiers' Performance: When and Why Failure Occurs?
Knowledge and Information Systems (KAIS), 18(1), 83-108.
Yuchuh Tang, Yan-Qing Zhang, Nitesh V. Chawla and Sven Kresser (February 2009).
SVMs Modeling for Highly Imbalanced Classification.
IEEE Transactions on Systems, Man and Cybernetics, Part B (SMCB), 39(1), 281-288.
2008
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David A. Cieslak and Nitesh V. Chawla (December 2008).
Start Globally, Optimize Locally, Predict Globally: Improving Performance on Unbalanced Data.
IEEE International Conference on Data Mining (ICDM), Pisa, Italy.
Christopher Moretti, Karsten Steinhaeuser, Douglas Thain and Nitesh V. Chawla (December 2008).
Scaling Up Classifiers to Cloud Computers.
IEEE International Conference on Data Mining (ICDM), Pisa, Italy.
Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Mohamed M. Gaber and Auroop R. Ganguly (December 2008).
Knowledge Discovery from Sensor Data.
ACM SIGKDD Explorations, 10(2), 68-73.
Darcy Davis, Nitesh V. Chawla, Nicholas Blumm, Nicholas A. Christakis, Albert-László Barabási (October 2008).
Predicting Individual Disease Risk Based on Medical History.
ACM Conference on Information and Knowledge Management (CIKM), Napa, CA.
David A. Cieslak, Nitesh V. Chawla and Douglas Thain (September 2008).
Troubleshooting Thousands of Jobs on Production Grids Using Data Mining Techniques.
IEEE/ACM International Conference on Grid Computing (GRID), Tsukuba, Japan.
David A. Cieslak and Nitesh V. Chawla (September 2008).
Learning Decision Trees for Unbalanced Data.
European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Antwerp, Belgium.
Qi Liao, David A. Cieslak, Aaron D. Striegel and Nitesh V. Chawla (June 2008).
Using Selective, Short-Term Memory to Improve Resilience Against DDoS Exhaustion Attacks.
Security and Communication Networks, 1(4), 287-299.
David A. Cieslak and Nitesh V. Chawla (May 2008).
Analyzing Classifier Performance on Imbalanced Datasets when Training and Testing Distributions Differ.
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Osaka, Japan.
Karsten Steinhaeuser and Nitesh V. Chawla (June 2008).
Is Modularity the Answer to Evaluating Community Structure in Networks?
International Workshop and Conference on Network Science (NetSci), Norwich, UK.
Karsten Steinhaeuser and Nitesh V. Chawla (April 2008).
Scalable Learning with Thread-Level Parallelism.
Midwest Artificial Intelligence and Cognitive Science Conference (MAICS), Cincinnati, OH.
Karsten Steinhaeuser and Nitesh V. Chawla (March 2008).
Community Detection in a Large Real-World Social Network.
Social Computing, Behavioral Modeling, and Prediction, Springer, 168-175.
Nitesh V. Chawla, David A. Cieslak, Lawrence O. Hall, Ajay Joshi (January 2008).
Automatically Countering Imbalance and Its Empirical Relationship to Cost.
Data Mining and Knowledge Discovery, 17(2), 225-252.
2007
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David A. Cieslak and Nitesh V. Chawla (December 2007).
Detecting Fracture Points in Classifier Performance.
IEEE International Conference on Data Mining (ICDM), Omaha, NE.
Alec Pawling, Nitesh V. Chawla and Gregory Madey (December 2007).
Anomaly Detection in Mobile Communication Networks.
Computational and Mathematical Organizational Theory, 13(4), 407-422.
Vince Thomas, Nitesh V. Chawla, Kevin W. Bowyer and Patrick J. Flynn (September 2007).
Learning to Predict Gender from Iris Images.
IEEE Conference on Biometrics: Theory, Applications and Systems (BTAS), Washington, DC.
Nitesh V. Chawla and Kevin W. Bowyer (July 2007).
Actively Exploring Creation of Face Spaces for Improved Face Recognition.
Conference on Artificial Intelligence (AAAI), Vancouver, Canada.
Michael J. Chapple, Nitesh V. Chawla and Aaron Striegel (June 2007).
Authentication Anomaly Detection: A Case Study on a Virtual Private Network.
ACM SIGMETRICS Workshop on Mining Network Data (MineNet), San Diego, CA.
Gregory R. Madey, Albert-László Barabási, Nitesh V. Chawla, Marta Gonzales, David Hachen, Brett Lantz, Alec Pawling, Timothy W. Schoenharl, Gábor Szabó, Pu Wang, Ping Yan (May 2007).
Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management.
International Conference on Computational Science (ICCS), Beijing, China.
Nitesh V. Chawla and Jared Sylvester (May 2007).
Exploiting Diversity in Ensembles: Improving Performance on Unbalanced Datasets.
International Workshop on Multiple Classifier Systems (MCS), Prague, Czech Republic.
Tanu Malik, Randal Burns and Nitesh V. Chawla (January 2007).
A Black-Box Approach to Query Cardinality Estimation.
ACM Conference on Innovative Data Systems Research (CIDM).
2006
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Tanu Malik, Randal Burns, Nitesh V. Chawla and Alexander S. Szalay (November 2006).
Estimating Query Result Sizes for Proxy Caching in Scientific Database Federations.
ACM/IEEE Supercomputing, Tampa, FL.
Yang Liu, Nitesh V. Chawla, Mary P. Harper, Elizabeth Shriberg and Andreas Stolcke (October 2006).
A Study in Machine Learning from Imbalanced Data for Sentence Boundary Detection in Speech.
Journal of Computer Speech and Language, 20(4), 468-494.
Karsten Steinhaeuser, Nitesh V. Chawla and Peter M. Kogge (September 2006).
Exploiting Thread-Level Parallelism to Build Decision Trees.
ECML/PKDD Workshop on Parallel and Distributed Data Mining, Berlin, Germany.
Dinesh Rajan, Christian Poellabauer and Nitesh V. Chawla (September 2006).
Resource Access Pattern Mining for Dynamic Energy Management.
ECML/PKDD Workshop on Automatic Computing: A New Challenge for Machine Learning, Berlin, Germany.
Alec Pawling, Nitesh V. Chawla and Amitabh Chaudhary (September 2006).
Evaluation of Summarization Schemes for Learning in Streams.
European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Berlin, Germany.
Nitesh V. Chawla and Xiangning Li (August 2006).
Pricing Scheme for Benefit Scoring.
ACM SIGKDD Workshop on Utility Based Data Mining (UBDM), Philadelphia, PA.
Danny Roobaert, Grigoris Karakoulas and Nitesh V. Chawla (August 2006).
Information Gain, Correlation and Support Vector Machines.
Feature Extraction: Foundations and Applications, Springer, 463-470.
Nitesh V. Chawla and David A. Cieslak (July 2006).
Evaluating Probability Estimates from Decision Trees.
AAAI Workshop on Evaluation Methods for Machine Learning, Boston, MA.
Jared Sylvester and Nitesh V. Chawla (July 2006).
Evolutionary Ensemble Creation and Thinning.
IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada.
Nitesh V. Chawla (July 2006).
Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees.
Machine Learning Challenges, Springer, 41-55.
Karsten Steinhaeuser, Nitesh V. Chawla and Christian Poellabauer (June 2006).
Towards Learning-Based Sensor Management.
First Workshop on Tackling Computer Systems Problems with Machine Learning (SysML), Saint-Malo, France.
David A. Cieslak, Douglas Thain and Nitesh V. Chawla (June 2006).
Troubleshooting Distributed Systems via Data Mining.
IEEE International Symposium on High Performance Distributed Computing (HPDC), Paris, France.
Alec Pawling, Nitesh V. Chawla and Gregory Madey (June 2006).
Anomaly Detection in a Mobile Communication Network.
Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), Notre Dame, IN.
David A. Cieslak, Nitesh V. Chawla and Aaron Striegel (May 2006).
Combating Imbalance in Network Intrusion Data.
IEEE International Conference on Granular Computing (GrC), Atlanta, GA.
2005
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Alec Pawling, Nitesh V. Chawla and Amitabh Chaudhary (November 2005).
Computing Information Gain in Data Streams.
IEEE ICDM Workshop on Temporal Data Mining, Houston, TX.
Nitesh V. Chawla and Kevin W. Bowyer (October 2005).
Ensembles in Face Recognition: Tackling the Extremes of High Dimensionality, Temporality, and Variance in Data.
IEEE International Conference on Systems, Man and Cybernetics (SMC), Big Island, Hawaii.
Nitesh V. Chawla (September 2005).
Data Mining for Imbalanced Datasets: An Overview.
Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Springer, 853-867.
Nitesh V. Chawla, Lawrence O. Hall and Ajay Joshi (August 2005).
Wrapper-Based Computation and Evaluation of Sampling Methods for Imbalanced Datasets.
ACM SIGKDD Workshop on Utility-Based Data Mining (UBDM), Chicago, IL.
Jared Sylvester and Nitesh V. Chawla (July 2005).
Evolutionary Ensembles: Combining Learning Agents Using Genetic Algorithms.
AAAI Workshop on Multi-Agent Systems, Pittsburgh, PA.
Nitesh V. Chawla and Kevin W. Bowyer (June 2005).
Random Subspaces and Subsampling for 2-D Face Recognition.
Computer Vision and Pattern Recognition, 2, 582-589.
Nitesh V. Chawla (June 2005).
Teaching Data Mining by Coalescing Theory and Applications.
International Conference on Frontiers in Education, Las Vegas, NV.
Nitesh V. Chawla and Kevin W. Bowyer (June 2005).
Designing Multiple Classifier Systems for Face Recognition.
International Workshop on Multiple Classifier Systems (MCS), Seaside, CA.
Daniel Mack, Nitesh V. Chawla and Gregory Madey (June 2005).
Activity Mining in Open Source Software.
Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), Notre Dame, IN.
Nitesh V. Chawla and Grigoris J. Karakoulas (March 2005).
Learning from Labeled and Unlabeled Data: An Empirical Study Across Techniques and Domains.
Journal of Artificial Intelligence Research, 23, 331-366.
2004
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Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer and W. Philip Kegelmeyer (December 2004).
Learning Ensembles from Bites: A Scalable and Accurate Approach.
Journal of Machine Learning Research, 5, 421-451.
Steven Eschrich, Nitesh V. Chawla and Lawrence O. Hall (November 2004).
Learning to Predict in Complex Biological Domains.
Journal of System Simulation, 14(11), 1464-1471.
Predrag Radivojac, Nitesh V. Chawla, A. Keith Dunker and Zoran Obradovic (August 2004).
Classification and Knowledge Discovery in Protein Databases.
Journal of Biomedical Informatics, 37(4), 224-239.
Nitesh V. Chawla, Nathalie Japkowicz and Aleksander Kolcz (June 2004).
Learning From Imbalanced Datasets.
ACM SIGKDD Explorations, 6(1), 1-6.
2003
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Nitesh V. Chawla, Grigoris Karakoulas and Danny Roobaert (December 2003).
Lessons Learned from the NIPS Feature Selection Challenge.
NIPS Workshop on Feature Selection, Vancouver, Canada.
Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall and Kevin W. Bowyer (September 2003)
SMOTEBoost: Improving the Prediction of the Minority Class in Boosting.
European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Cavtat-Dubrovnik, Croatia.
Nitesh V. Chawla (August 2003).
C4.5 and Imbalanced Data Sets: Investigating the Effect of Sampling Method, Probabilistic Estimate, and Decision Tree Structure.
ICML Workshop on Learning from Imbalanced Data Sets II, Washington, DC.
Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer and Clayton Springer (January 2003).
Distributed Learning with Bagging-Like Performance.
Pattern Recognition Letters, 24(1),455-471.
2002
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Steven Eschrich, Nitesh V. Chawla and Lawrence O. Hall (July 2002).
Generalization Methods in Bioinformatics.
ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD), Edmonton, Canada.
Nitesh V. Chawla, Kevin W. Bowyer, Thomas E. Moore and Philip Kegelmeyer (June 2002).
SMOTE: Synthetic Minority Over-Sampling Technique.
Journal of Artificial Intelligence Research, 16, 321-357.
Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer (June 2002).
Distributed Pasting of Small Votes.
International Workshop on Multiple Classifier Systems (MCS), Cagliari, Italy.
2001
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Nitesh V. Chawla, Steven Eschrich and Lawrence O. Hall (November 2001).
Creating Ensembles of Classifiers.
IEEE International Conference on Data Mining (ICDM), San Jose, CA.
Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer and W. Philip Kegelmeyer (August 2001).
Investigation of Bagging-Like Effects and Decision Trees Versus Neural Nets in Protein Secondary Structure Prediction.
ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD), San Francisco, CA.
Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer and W. Philip Kegelmeyer (August 2001).
Bagging is a Small-Data-Set Phenomenon.
Computer Vision and Pattern Recognition, 2, 684-689.
2000
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Kevin W. Bowyer, Lawrence O. Hall, Thomas E. Moore, Nitesh V. Chawla and W. Phillip Kegelmeyer (October 2000).
A Parallel Decision Tree Builder for Mining Very Large Visualization Datasets.
IEEE International Conference on Systems, Man and Cybernetics (SMC), Nashville, TN.
Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer and W. Philip Kegelmeyer (January 2000).
Learning Rules from Distributed Data.
Large-Scale Parallel Data Mining, LNAI, Springer, 211-220.
1999
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Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer and W. Philip Kegelmeyer (August 1999).
Learning Rules from Distributed Data.
ACM SIGKDD Workshop on Large-Scale Parallel Data Mining, San Diego, CA.
1998
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Lawrence O. Hall, Nitesh V. Chawla and Kevin W. Bowyer (August 1998).
Combining Decision Trees Learned in Parallel.
ACM SIGKDD Workshop on Distributed Data Mining, New York, NY.
Lawrence O. Hall, Nitesh V. Chawla and Kevin W. Bowyer (July 1998).
Decision Tree Learning on Very Large Data Sets.
IEEE International Conference on Systems, Man and Cybernetics (SMC), San Diego, CA.