Constantin F. Aliferis

Constantin F. Aliferis, Gregory F. Cooper: A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modeling Techniques. CoRR abs/1302.3552 (2013)

Constantin F. Aliferis, Gregory F. Cooper: An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief Networks Usin. CoRR abs/1302.6779 (2013)

Lawrence D. Fu, Yindalon Aphinyanaphongs, Lily Wang, Constantin F. Aliferis: A comparison of evaluation metrics for biomedical journals, articles, and websites in terms of sensitivity to topic. Journal of Biomedical Informatics 44(4): 587-594 (2011)

Isabelle Guyon, Alexander R. Statnikov, Constantin F. Aliferis: Time Series Analysis with the Causality Workbench. Journal of Machine Learning Research - Proceedings Track 12: 115-139 (2011)

Subramani Mani, Constantin F. Aliferis, Alexander R. Statnikov: Bayesian Algorithms for Causal Data Mining. Journal of Machine Learning Research - Proceedings Track 6: 121-136 (2010)

Alexander R. Statnikov, Constantin F. Aliferis: TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries. Journal of Machine Learning Research - Proceedings Track 6: 249-256 (2010)

Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos: Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation. Journal of Machine Learning Research 11: 171-234 (2010)

Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos: Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions. Journal of Machine Learning Research 11: 235-284 (2010)

Alexander R. Statnikov, Constantin F. Aliferis: Analysis and Computational Dissection of Molecular Signature Multiplicity. PLoS Computational Biology 6(5) (2010)

Lawrence D. Fu, Constantin F. Aliferis: Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature. Scientometrics 85(1): 257-270 (2010)

Nafeh Fananapazir, Alexander R. Statnikov, Constantin F. Aliferis: The FAST-AIMS Clinical Mass Spectrometry Analysis System. Adv. Bioinformatics 2009 (2009)

Alexander R. Statnikov, Lily Wang, Constantin F. Aliferis: A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 9 (2008)

Isabelle Guyon, Constantin F. Aliferis, Gregory F. Cooper, André Elisseeff, Jean-Philippe Pellet, Peter Spirtes, Alexander R. Statnikov: Design and Analysis of the Causation and Prediction Challenge. Journal of Machine Learning Research - Proceedings Track 3: 1-33 (2008)

Subramani Mani, Constantin F. Aliferis: A Causal Modeling Framework for Generating Clinical Practice Guidelines from Data. AIME 2007: 446-450

Lawrence D. Fu, Lily Wang, Yindalon Aphinyanagphongs, Constantin F. Aliferis: A Comparison of Impact Factor, Clinical Query Filters, and Pattern Recognition Query Filters in Terms of Sensitivity to Topic. MedInfo 2007: 716-720

Subramani Mani, Constantin F. Aliferis, Shanthi Krishnaswami, Theodore Kotchen: Learning Causal and Predictive Clinical Practice Guidelines from Data. MedInfo 2007: 850-854

Yindalon Aphinyanaphongs, Constantin F. Aliferis: Text Categorization Models for Identifying Unproven Cancer Treatments on the Web. MedInfo 2007: 968-972

Elmer V. Bernstam, Jorge R. Herskovic, Yindalon Aphinyanaphongs, Constantin F. Aliferis, Madurai G. Sriram, William R. Hersh: Research Paper: Using Citation Data to Improve Retrieval from MEDLINE. JAMIA 13(1): 96-105 (2006)

Yindalon Aphinyanaphongs, Alexander R. Statnikov, Constantin F. Aliferis: Research Paper: A Comparison of Citation Metrics to Machine Learning Filters for the Identification of High Quality MEDLINE Documents. JAMIA 13(4): 446-455 (2006)

Ioannis Tsamardinos, Laura E. Brown, Constantin F. Aliferis: The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learning 65(1): 31-78 (2006)

Ioannis Tsamardinos, Alexander R. Statnikov, Laura E. Brown, Constantin F. Aliferis: Generating Realistic Large Bayesian Networks by Tiling. FLAIRS Conference 2006: 592-597

Alexander R. Statnikov, Constantin F. Aliferis, Ioannis Tsamardinos, Douglas P. Hardin, Shawn Levy: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21(5): 631-643 (2005)

Alexander R. Statnikov, Ioannis Tsamardinos, Yerbolat Dosbayev, Constantin F. Aliferis: GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data. I. J. Medical Informatics 74(7-8): 491-503 (2005)

Yindalon Aphinyanaphongs, Ioannis Tsamardinos, Alexander R. Statnikov, Douglas P. Hardin, Constantin F. Aliferis: Research Paper: Text Categorization Models for High-Quality Article Retrieval in Internal Medicine. JAMIA 12(2): 207-216 (2005)

Gregory F. Cooper, Vijoy Abraham, Constantin F. Aliferis, John M. Aronis, Bruce G. Buchanan, Rich Caruana, Michael J. Fine, Janine E. Janosky, Gary Livingston, Tom M. Mitchell: Predicting dire outcomes of patients with community acquired pneumonia. Journal of Biomedical Informatics 38(5): 347-366 (2005)

Laura E. Brown, Ioannis Tsamardinos, Constantin F. Aliferis: A Comparison of Novel and State-of-the-Art Polynomial Bayesian Network Learning Algorithms. AAAI 2005: 739-745

Alexander R. Statnikov, Ioannis Tsamardinos, Constantin F. Aliferis: Using the GEMS System for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data. AAAI 2005: 1710-1711

Douglas P. Hardin, Ioannis Tsamardinos, Constantin F. Aliferis: A theoretical characterization of linear SVM-based feature selection. ICML 2004

Constantin F. Aliferis, Ioannis Tsamardinos, P. Massion, Alexander R. Statnikov, Nafeh Fananapazir, Douglas P. Hardin: Machine Learning Models for Classification of Lung Cancer and Selection of Genomic Markers Using Array Gene Expression Data. FLAIRS Conference 2003: 67-71

Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov: Algorithms for Large Scale Markov Blanket Discovery. FLAIRS Conference 2003: 376-381

Lewis Frey, Douglas H. Fisher, Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov: Identifying Markov Blankets with Decision Tree Induction. ICDM 2003: 59-66

Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov: Time and sample efficient discovery of Markov blankets and direct causal relations. KDD 2003: 673-678

Constantin F. Aliferis, Ioannis Tsamardinos, P. Massion, Alexander R. Statnikov, Douglas P. Hardin: Why Classification Models Using Array Gene Expression Data Perform So Well: A Preliminary Investigation of Explanatory Factors. METMBS 2003: 47-53

Constantin F. Aliferis, Ioannis Tsamardinos, Alexander R. Statnikov, Laura E. Brown: Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery. METMBS 2003: 371-376

Gregory F. Cooper, Constantin F. Aliferis, Richard Ambrosino, John M. Aronis, Bruce G. Buchanan, Rich Caruana, Michael J. Fine, Clark Glymour, Geoffrey J. Gordon, Barbara H. Hanusa, Janine E. Janosky, Christopher Meek, Tom M. Mitchell, Thomas S. Richardson, Peter Spirtes: An evaluation of machine-learning methods for predicting pneumonia mortality. Artificial Intelligence in Medicine 9(2): 107-138 (1997)

Constantin F. Aliferis, Gregory F. Cooper: A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modeling Techniques. UAI 1996: 28-39

Constantin F. Aliferis, Gregory F. Cooper: An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief Networks Using Simulated Data Sets. UAI 1994: 8-14