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ORCIDPhilip Chan 0001
Person information
- affiliation: Florida Institute of Technology, Department of Computer Sciences, Melbourne, FL, USA
Other persons with the same name
- Philip Chan — disambiguation page
- Philip Chan 0002 — Monash University, Caulfield School of Information Technology, VIC, Australia
- Philip Chan 0003 — University of Queensland, Brisbane, Australia
Other persons with a similar name
- Philip C. Chan
- Philip C. H. Chan
- Philip Ka-Fai Chan — DePaul University, School of Computer Science, Telecommunication and Information Systems, Chicago, IL, USA
- Philip L. Y. Chan
- Philip Wing Keung Chan
- Philip Y. Chan
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2020 – today
- 2026
[i7]Spiridon Kasapis, Pouya Hosseinzadeh, Kathryn Whitman, Ricky Egeland, Manolis K. Georgoulis, Angelos Vourlidas, Athanasios Papaioannou, Eleni Lavasa, Anastasios Anastasiadis, Giorgos Giannopoulos, Andrés Muñoz-Jaramillo, Bala Poduval, Irina N. Kitiashvili, Alexander G. Kosovichev, Viacheslav Sadykov, Soukaina Filali Boubrahimi, Tate T. Hutchins, Hameedullah Farooki, Manuel E. Cuesta, Leng Y. Khoo, Sungmin Pak, Robert Czarnota, Jamie S. Rankin, Jamey Szalay, Mitchell M. Shen, Georgios Livadiotis, Zigong Xu, David J. McComas, Nikolaos Sarlis, Dionissios Hristopulos, Arik Posner, Alec J. Engell, Mohammed AbuBakr Ali
, Ali G. A. Abdelkawy, Abdelrazek M. K. Shaltout, M. M. Beheary, Christina O. Lee, Sigiava Aminalragia-Giamini, Constantinos Papadimitriou, Ingmar Sandberg, Savvas Raptis, Shah Muhammad Hamdi, Monica Laurenza, Mirko Stumpo, Sumanth A. Rotti, India Jackson, Aatiya Ali, Atilim Gunes Baydin, Nathan Schwadron, Subhamoy Chatterjee, Maher A. Dayeh, Gelu M. Nita, Patrick O'Keefe, Chun Jie Chong, Paul Kosovich, Russell Marroquin, Berkay Aydin, Petrus C. Martens, Lulu Zhao, Yang Chen, Yian Yu, Monica G. Bobra, Ward Manchester, Tamas Gombosi, Ming Zhang, Jesse Torres, Philip K. Chan, Mohamed Nedal, Kamen Kozarev, Peijin Zhang, Kimberly Moreland, Hazel M. Bain, Samuel Hart, Michael J. Starkey, Alan G. Ling, Simone Benella:
Review of Machine Learning Models for Solar Energetic Particle Prediction. CoRR abs/2606.19539 (2026)- 2025
- [c45]Josias K. Moukpe, Philip K. Chan, Ming Zhang:
Highly Imbalanced Regression with Tabular Data in SEP and Other Applications. ICMLA 2025: 404-409 - [i6]Josias K. Moukpe, Philip K. Chan, Ming Zhang:
Highly Imbalanced Regression with Tabular Data in SEP and Other Applications. CoRR abs/2509.16339 (2025) - 2023
- [c44]Jingyun Jia, Philip K. Chan:
GII: A Unified Approach to Representation Learning in Open Set Recognition with Novel Category Discovery. ICANN (8) 2023: 243-254 - [c43]Jingyun Jia, Philip K. Chan:
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition. IJCNN 2023: 1-8 - 2022
- [c42]Jingyun Jia, Philip K. Chan:
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition. ICANN (4) 2022: 471-483 - [c41]Jingyun Jia, Philip K. Chan:
Representation Learning with Function Call Graph Transformations for Malware Open Set Recognition. IJCNN 2022: 1-8 - [i5]Jingyun Jia, Philip K. Chan:
Representation learning with function call graph transformations for malware open set recognition. CoRR abs/2205.06918 (2022) - [i4]Jingyun Jia, Philip K. Chan:
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition. CoRR abs/2209.14385 (2022) - 2021
- [c40]Jingyun Jia, Philip K. Chan:
MMF: A Loss Extension for Feature Learning in Open Set Recognition. ICANN (2) 2021: 319-331 - [i3]Jingyun Jia, Philip K. Chan:
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition. CoRR abs/2105.13557 (2021) - 2020
- [c39]Mehadi Hassen, Philip K. Chan:
Unsupervised Open Set Recognition using Adversarial Autoencoders. ICMLA 2020: 360-365 - [c38]Mehadi Hassen, Philip K. Chan:
Learning a Neural-network-based Representation for Open Set Recognition. SDM 2020: 154-162 - [i2]Jingyun Jia, Philip K. Chan:
MMF: A loss extension for feature learning in open set recognition. CoRR abs/2006.15117 (2020)
2010 – 2019
- 2018
- [j18]Xunhu Sun, Philip K. Chan:
Estimating effectiveness of twitter messages with a personalized machine learning approach. Knowl. Inf. Syst. 56(1): 27-53 (2018) - [c37]Ebad Ahmadzadeh, Philip K. Chan:
Identifying Pros and Cons of Product Aspects Based on Customer Reviews. IEEE BigData 2018: 931-936 - [c36]Mehadi Hassen, Philip K. Chan:
Learning to Identify Known and Unknown Classes: A Case Study in Open World Malware Classification. FLAIRS 2018: 26-31 - [c35]Lingfeng Zhang, Philip K. Chan:
Detecting Harmful Hand Behaviors with Machine Learning from Wearable Motion Sensor Data. FLAIRS 2018: 323-328 - [c34]Huizhong Hu, Philip K. Chan:
Using A Personalized Anomaly Detection Approach with Machine Learning to Detect Stolen Phones. FLAIRS 2018: 410-415 - [i1]Mehadi Hassen, Philip K. Chan:
Learning a Neural-network-based Representation for Open Set Recognition. CoRR abs/1802.04365 (2018) - 2017
- [c33]Ebad Ahmadzadeh, Philip K. Chan:
Mining pros and cons of actions from social media for decision support. IEEE BigData 2017: 877-882 - [c32]Mehadi Hassen, Philip K. Chan:
Scalable Function Call Graph-based Malware Classification. CODASPY 2017: 239-248 - [c31]Mehadi Hassen, Marco M. Carvalho, Philip K. Chan:
Malware classification using static analysis based features. SSCI 2017: 1-7 - [r2]Philip K. Chan:
Machine Learning for IT Security. Encyclopedia of Machine Learning and Data Mining 2017: 788-790 - 2016
- [c30]Philip K. Chan, Ebad Ahmadzadeh:
Improving efficiency of maximizing spread in the flow authority model for large sparse networks. IEEE BigData 2016: 863-868 - 2014
- [j17]Nicholas C. Miller, Philip K. Chan:
Semantic Search Techniques for Learning Smaller Boolean Expression Trees in Genetic Programming. Int. J. Comput. Intell. Appl. 13(3) (2014) - [c29]Xunhu Sun, Philip K. Chan:
An Analysis of Instance Selection for Neural Networks to Improve Training Speed. ICMLA 2014: 288-293 - 2012
- [c28]Kleber A. Garcia, Philip K. Chan:
Estimating Hospital Admissions with a Randomized Regression Approach. ICMLA (1) 2012: 179-184 - 2010
- [j16]Gaurav Tandon, Philip K. Chan:
Increasing coverage to improve detection of network and host anomalies. Mach. Learn. 79(3): 307-334 (2010) - [c27]Denis Petrussenko, Philip K. Chan:
Incrementally Learning Rules for Anomaly Detection. FLAIRS 2010 - [r1]Philip K. Chan:
Machine Learning for IT Security. Encyclopedia of Machine Learning 2010: 637-639
2000 – 2009
- 2009
- [c26]Gaurav Tandon, Philip K. Chan:
Tracking User Mobility to Detect Suspicious Behavior. SDM 2009: 871-882 - 2008
- [j15]Hyoung-rae Kim, Philip K. Chan:
Learning implicit user interest hierarchy for context in personalization. Appl. Intell. 28(2): 153-166 (2008) - 2007
- [j14]Philip K. Chan, Ronaldo Menezes, Debasis Mitra, Eraldo Ribeiro, Marius Silaghi:
Intelligent Systems at Florida Tech. IEEE Intell. Informatics Bull. 8(1): 5-6 (2007) - [j13]Stan Salvador, Philip Chan:
Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5): 561-580 (2007) - [c25]Gaurav Tandon, Philip K. Chan:
Weighting versus pruning in rule validation for detecting network and host anomalies. KDD 2007: 697-706 - 2006
- [j12]Gaurav Tandon, Philip K. Chan:
On the Learning of System Call Attributes for Host-based Anomaly Detection. Int. J. Artif. Intell. Tools 15(6): 875-892 (2006) - [j11]Philip K. Chan, Richard Lippmann:
Machine Learning for Computer Security. J. Mach. Learn. Res. 7: 2669-2672 (2006) - 2005
- [j10]Stan Salvador, Philip Chan:
Learning States and Rules for Detecting Anomalies in Time Series. Appl. Intell. 23(3): 241-255 (2005) - [j9]Dragos D. Margineantu, Stephen Bay, Philip Chan, Terran Lane:
Data mining methods for anomaly detection KDD-2005 workshop report. SIGKDD Explor. 7(2): 132-136 (2005) - [c24]Gaurav Tandon, Philip K. Chan:
Learning Useful System Call Attributes for Anomaly Detection. FLAIRS 2005: 405-411 - [c23]Philip K. Chan, Matthew V. Mahoney:
Modeling Multiple Time Series for Anomaly Detection. ICDM 2005: 90-97 - [c22]Hyoung-rae Kim, Philip K. Chan:
Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks. WebKDD 2005: 158-176 - [c21]Hyoung-rae Kim, Philip K. Chan:
Implicit Indicators for Interesting Web Pages. WEBIST 2005: 270-277 - 2004
- [j8]Wei Fan, Matthew Miller, Salvatore J. Stolfo, Wenke Lee, Philip K. Chan:
Using artificial anomalies to detect unknown and known network intrusions. Knowl. Inf. Syst. 6(5): 507-527 (2004) - [c20]Stan Salvador, Philip Chan, John Brodie:
Learning States and Rules for Time Series Anomaly Detection. FLAIRS 2004: 306-311 - [c19]Hyoung-rae Kim, Philip K. Chan:
Identifying Variable-Length Meaningful Phrases with Correlation Functions. ICTAI 2004: 30-38 - [c18]Stan Salvador, Philip Chan:
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms. ICTAI 2004: 576-584 - [c17]Gaurav Tandon, Debasis Mitra, Philip K. Chan:
Motif-Oriented Representation of Sequences for a Host-Based Intrusion Detection System. IEA/AIE 2004: 605-615 - [c16]Gaurav Tandon, Philip K. Chan, Debasis Mitra:
MORPHEUS: motif oriented representations to purge hostile events from unlabeled sequences. VizSEC 2004: 16-25 - [e1]Carla E. Brodley, Philip Chan, Richard Lippmann, William Yurcik:
1st ACM Workshop on Visualization and Data Mining for Computer Security, VizSEC/DMSEC 2004, Washington, DC, USA, October 29, 2004. ACM 2004, ISBN 1-58113-974-8 [contents] - 2003
- [c15]Matthew V. Mahoney, Philip K. Chan:
Learning Rules for Anomaly Detection of Hostile Network Traffic. ICDM 2003: 601-604 - [c14]Hyoung R. Kim, Philip K. Chan:
Learning implicit user interest hierarchy for context in personalization. IUI 2003: 101-108 - [c13]Matthew V. Mahoney, Philip K. Chan:
An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection. RAID 2003: 220-237 - 2002
- [c12]Matthew V. Mahoney, Philip K. Chan:
Learning nonstationary models of normal network traffic for detecting novel attacks. KDD 2002: 376-385 - 2001
- [j7]Salvatore J. Stolfo, Wenke Lee, Philip K. Chan, Wei Fan, Eleazar Eskin:
Data Mining-based Intrusion Detectors: An Overview of the Columbia IDS Project. SIGMOD Rec. 30(4): 5-14 (2001) - [c11]Wei Fan, Matthew Miller, Salvatore J. Stolfo, Wenke Lee, Philip K. Chan:
Using Artificial Anomalies to Detect Unknown and Known Network Intrusions. ICDM 2001: 123-130
1990 – 1999
- 1999
- [j6]Philip Chan, Wei Fan, Andreas L. Prodromidis, Salvatore J. Stolfo:
Distributed data mining in credit card fraud detection. IEEE Intell. Syst. 14(6): 67-74 (1999) - [j5]Philip K. Chan, Salvatore J. Stolfo, David H. Wolpert:
Guest Editors' Introduction. Mach. Learn. 36(1-2): 5-7 (1999) - [c10]Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip K. Chan:
AdaCost: Misclassification Cost-Sensitive Boosting. ICML 1999: 97-105 - [c9]Philip K. Chan:
Constructing Web User Profiles: A non-invasive Learning Approach. WebKDD 1999: 39-55 - 1998
- [c8]Philip K. Chan, Salvatore J. Stolfo:
Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection. KDD 1998: 164-168 - [d1]Salvatore J. Stolfo, Wei Fan, Wenke Lee, Andreas L. Prodromidis, Philip Chan:
KDD Cup 1999 Data. UCI-MLR, 1998
- 1997
- [j4]Philip K. Chan, Salvatore J. Stolfo:
On the Accuracy of Meta-Learning for Scalable Data Mining. J. Intell. Inf. Syst. 8(1): 5-28 (1997) - [c7]Salvatore J. Stolfo, Andreas L. Prodromidis, Shelley Tselepis, Wenke Lee, Dave W. Fan, Philip K. Chan:
JAM: Java Agents for Meta-Learning over Distributed Databases. KDD 1997: 74-81 - 1996
- [c6]Philip K. Chan, Salvatore J. Stolfo:
Sharing Learned Models among Remote Database Partitions by Local Meta-Learning. KDD 1996: 2-7 - 1995
- [c5]Philip K. Chan, Salvatore J. Stolfo:
A Comparative Evaluation of Voting and Meta-learning on Partitioned Data. ICML 1995: 90-98 - [c4]Philip K. Chan, Salvatore J. Stolfo:
Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning. KDD 1995: 39-44 - 1993
- [j3]Christopher J. Matheus, Philip K. Chan, Gregory Piatetsky-Shapiro:
Systems for Knowledge Discovery in Databases. IEEE Trans. Knowl. Data Eng. 5(6): 903-913 (1993) - [c3]Philip K. Chan, Salvatore J. Stolfo:
Experiments on Multi-Strategy Learning by Meta-Learning. CIKM 1993: 314-323 - [c2]Philip K. Chan, Salvatore J. Stolfo:
Toward Multi-Strategy Parallel & Distributed Learning in Sequence Analysis. ISMB 1993: 65-73 - 1991
- [j2]Salvatore J. Stolfo, Ouri Wolfson, Philip K. Chan, Hasanat M. Dewan, Leland Woodbury, Jason S. Glazier, David Ohsie:
PARULE: Parallel Rule Processing Using Meta-rules for Redaction. J. Parallel Distributed Comput. 13(4): 366-382 (1991) - 1990
- [j1]Douglas H. Fisher, Philip K. Chan:
Statistical guidance in symbolic learning. Ann. Math. Artif. Intell. 2: 135-147 (1990)
1980 – 1989
- 1989
- [c1]Philip K. Chan:
Inductive Learning with BCT. ML 1989: 104-108
Coauthor Index
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