| 2025 | ICMLA | Highly Imbalanced Regression with Tabular Data in SEP and Other Applications. | Josias K. Moukpe, Philip K. Chan, Ming Zhang |
| 2023 | ICANN | GII: A Unified Approach to Representation Learning in Open Set Recognition with Novel Category Discovery. | Jingyun Jia, Philip K. Chan |
| 2023 | IJCNN | Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition. | Jingyun Jia, Philip K. Chan |
| 2022 | ICANN | Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition. | Jingyun Jia, Philip K. Chan |
| 2022 | IJCNN | Representation Learning with Function Call Graph Transformations for Malware Open Set Recognition. | Jingyun Jia, Philip K. Chan |
| 2021 | ICANN | MMF: A Loss Extension for Feature Learning in Open Set Recognition. | Jingyun Jia, Philip K. Chan |
| 2020 | ICMLA | Unsupervised Open Set Recognition using Adversarial Autoencoders. | Mehadi Hassen, Philip K. Chan |
| 2020 | SDM | Learning a Neural-network-based Representation for Open Set Recognition. | Mehadi Hassen, Philip K. Chan |
| 2018 | FlAIRS | Learning to Identify Known and Unknown Classes: A Case Study in Open World Malware Classification. | Mehadi Hassen, Philip K. Chan |
| 2018 | FlAIRS | Using A Personalized Anomaly Detection Approach with Machine Learning to Detect Stolen Phones. | Huizhong Hu, Philip K. Chan |
| 2018 | FlAIRS | Detecting Harmful Hand Behaviors with Machine Learning from Wearable Motion Sensor Data. | Lingfeng Zhang, Philip K. Chan |
| 2014 | ICMLA | An Analysis of Instance Selection for Neural Networks to Improve Training Speed. | Xunhu Sun, Philip K. Chan |
| 2012 | ICMLA | Estimating Hospital Admissions with a Randomized Regression Approach. | Kleber A. Garcia, Philip K. Chan |
| 2010 | FlAIRS | Incrementally Learning Rules for Anomaly Detection. | Denis Petrussenko, Philip K. Chan |
| 2009 | SDM | Tracking User Mobility to Detect Suspicious Behavior. | Gaurav Tandon, Philip K. Chan |
| 2007 | KDD | Weighting versus pruning in rule validation for detecting network and host anomalies. | Gaurav Tandon, Philip K. Chan |
| 2005 | FlAIRS | Learning Useful System Call Attributes for Anomaly Detection. | Gaurav Tandon, Philip K. Chan |
| 2005 | ICDM | Modeling Multiple Time Series for Anomaly Detection. | Philip K. Chan, Matthew V. Mahoney |
| 2005 | KDD | Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks. | Hyoung-rae Kim, Philip K. Chan |
| 2005 | WEBIST | Implicit Indicators for Interesting Web Pages. | Hyoung-rae Kim, Philip K. Chan |
| 2004 | ICTAI | Identifying Variable-Length Meaningful Phrases with Correlation Functions. | Hyoung-rae Kim, Philip K. Chan |
| 2004 | VizSec | MORPHEUS: motif oriented representations to purge hostile events from unlabeled sequences. | Gaurav Tandon, Philip K. Chan, Debasis Mitra |
| 2003 | ICDM | Learning Rules for Anomaly Detection of Hostile Network Traffic. | Matthew V. Mahoney, Philip K. Chan |
| 2003 | IUI | Learning implicit user interest hierarchy for context in personalization. | Hyoung R. Kim, Philip K. Chan |
| 2003 | RAID | An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection. | Matthew V. Mahoney, Philip K. Chan |
| 2002 | KDD | Learning nonstationary models of normal network traffic for detecting novel attacks. | Matthew V. Mahoney, Philip K. Chan |
| 2001 | ICDM | Using Artificial Anomalies to Detect Unknown and Known Network Intrusions. | Wei Fan, Matthew Miller, Salvatore J. Stolfo, Wenke Lee, Philip K. Chan |
| 1999 | ICML | AdaCost: Misclassification Cost-Sensitive Boosting. | Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip K. Chan |
| 1999 | KDD | Constructing Web User Profiles: A non-invasive Learning Approach. | Philip K. Chan |
| 1998 | KDD | Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection. | Philip K. Chan, Salvatore J. Stolfo |
| 1997 | KDD | JAM: Java Agents for Meta-Learning over Distributed Databases. | Salvatore J. Stolfo, Andreas L. Prodromidis, Shelley Tselepis, Wenke Lee, Dave W. Fan, Philip K. Chan |
| 1996 | KDD | Sharing Learned Models among Remote Database Partitions by Local Meta-Learning. | Philip K. Chan, Salvatore J. Stolfo |
| 1995 | ICML | A Comparative Evaluation of Voting and Meta-learning on Partitioned Data. | Philip K. Chan, Salvatore J. Stolfo |
| 1995 | KDD | Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning. | Philip K. Chan, Salvatore J. Stolfo |
| 1993 | CIKM | Experiments on Multi-Strategy Learning by Meta-Learning. | Philip K. Chan, Salvatore J. Stolfo |
| 1993 | ISMB | Toward Multi-Strategy Parallel & Distributed Learning in Sequence Analysis. | Philip K. Chan, Salvatore J. Stolfo |
| 1989 | ICML | Inductive Learning with BCT. | Philip K. Chan |