Publications

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2016

Decrypting “cryptogenic” epilepsy: Semi-supervised Hierarchical Conditional Random Fields for detecting cortical lesions in MRI-negative patients
Ahmed, B., Thesen, T., Blackmon, K., Kuzniecky,R., Devinsky, O., and Brodley, C. E., “Decrypting “cryptogenic” epilepsy: Semi-supervised Hierarchical Conditional Random Fields for detecting cortical lesions in MRI-negative patients,” The Journal of Machine Learning Research, Special topic issue on learning from electronic health data, 17(1), pp. 3885-3914, 2016.

Ahmed, B., Thesen, T., Blackmon, K., Kuzniecky, R., Devinsky, O., Dy, J. G., and Brodley, C. E. “Multi-task learning with weak class labels: Leveraging iEEG to detect cortical lesions in Cryptogenic Epilepsy,” Proceedings of the 2016 Machine Learning in Healthcare Conference (MLHC 16), 2016.

Zhao, Y., Ahmed, B., Thesen, T., Blackmon, K., Dy, J., Kuzniekcy, R., Devinsky, O., and Brodley, C. E., “A non-parametric approach to detect epileptogenic lesions using Restricted Boltzmann Machines, 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2016.

2015

Finding homogeneous groups of multiple sclerosis patients by removing physician subjectiving via constraint-based clustering
Liu, J., Healy, B., Chitnis, T., Brodley, C. E., “Finding homogeneous groups of multiple sclerosis patients by removing physician subjectiving via constraint-based clustering,” AI and Medicine, 2015.

Clinical Vestibular Testing Assessed With Machine-Learning Algorithms 
Priesol, A. J., Cao, M., Brodley, C. E., Lewis, R. F., “Clinical vestibular testing assessed with machine learning algorithms,” JAMA Otolaryngology-Head & Neck Surgery, 2015.

Cortical feature analysis and machine learning improves detection of “MRI-negative” focal cortical dysplasia
Ahmed, B., Thesen, T., Barah, G., Carlson, C., Kuzniecky, R., Doyle, W., Blackmon, K., Devinsky, O., and Brodley, C. E., “Cortical feature analysis and machine learning improves detection of MRI-negative focal cortical dysplasia,” Science Direct, Epilepsy & Behavior, 2015.

Zhao, Z., Chitnis, T.,Healy, B., Dy, J., and Brodley, C. E., “Domain induced Dirichlet mixture of Gaussian Processes: An application to predicting disease progression in Multiple Sclerosis Patients,” IEEE International Conference on Data Mining (ICDM), 2015.

2014

Noto, K., Brodley, C. E., Majidi, S., Bianchi, D. and Slonim, D. “Characterizing systematic anomalies in eXpression data,” RECOMB, Pittsburgh, PA, April 2014.

Zhao, Y., Brodley, C. E., Chitnis, T., and Healy, B. “Addressing human subjectivity via transfer learning: An application to predicting disease course in multiple sclerosis patients,” in the Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), Philadelphia, PA, April 2014.

Ahmed, B., Thesen, T., Blackmon, K., Kuzniecky, R., Carlson, C., Quinn, B., Doyle, W., French, J., Devinsky, O., and Brodley, C. E., “Machine learning for detection of MRI-elusive epileptogenic lesions: A surface-based MRI morphometric approach,” SDM Workshop: Data Mining for Medicine and Healthcare, Philadelphia, PA, April 2014.

Moran, K., Wallace, B., and Brodley, C. E., “Discovering better AAAI keywords via clustering with crowd-sourced constraints,” the Twenty-eighth Conference on Artificial Intelligence, Quebec City, Canada, 2014.

Ahmed, B., Thesen, T., Blackmon, K., Devinsky, O., Kuznieky, R., and Brodley, C. E., “Hierarchical conditional random fields for unsupervised outlier detection: An application to detecting epileptogenic cortical malformations,” The Thirty-first International Conference on Machine Learning, China June 2014.

2013

Liu, J., Brodley, C. E., Healy, B., Chitnis, T. “Removing confounding factors via constraintbased clustering: An application to finding homogeneous groups of MS patients,” Data Mining for Healthcare Workshop at ICHI (included in ICHI conference proceedings), September 2013, Philadelphia, PA (invited for submission to the special issue of AI and Medicine on the best of ICHI).

Ahmed, B., Brodley, C. E., Carlson, C.,Kuzniecky, R., Devinsky, O., French, J., and Thesen, T., “Detection of structural brain abnormalities in single patients: Focal Epilepsy,” International Conference on Basic and Clinical Multimodal Imaging, Geneva, September, 2013.

Active literature discovery for scoping evidence reviews: How many needles are there?
Wallace, B., Dahabreh, I., Moran, K., Brodley, C. E., and Trikalinos, T. Active literature discovery for scoping evidence reviews: How many needles are there? KDD 2013 – Workshop on Data Mining for Healthcare (DMH), 2013.

2012

Wallace, B., Small, K., Brodley, C. E., Lau, J. and Trikalinos, T., “Deploying an interactive machine learning system in an evidence-based practice center,” In Proc. of the Second ACM International Health Informatics Symposium (IHI), January 2012, Miami, FL, pp. 819-826.

Towards modernizing the systematic review pipeline: Efficient updating via data mining
Wallace, B., Small, K., Brodley, C. E., Lau, J., Schmid, C., Bertram,Li., Lill, C., Cohen, J. and Trikalinos, T. “Towards modernizing the systematic review pipeline: Efficient updating via data mining,” Genetics in Medicine, vol. 14, pp 663-669, 2012.

Brown, E., Liu, J., Brodley, C. E., and Chang, R., “Dis-Function: Learning distance functions interactively,” IEEE Conference on Visual Analytics Science and Technology (VAST), October 2012, Seattle, WA, pp. 83-92.

Rebbapragada, U., Brodley, C. E., Sulla-Menashe, D., and Friedl, M., “Active label correction,” IEEE 12th International Conference on Data Mining (ICDM), December 2012, Brussels, pp. 1080- 1035.

Challenges and opportunities in applied machine learning
Brodley, C. E., Rebbapragada, U., Small, K., and Wallace, B., “Challenges and opportunities in applied machine learning,” AI Magazine, 33 (1), pp. 11-24, 2012.

2011

Single cell time resolved quorum responses reveal dependence on cell density and configuration
Whitaker, R. D., Pember, S., Wallace, B. C., Brodley, C. E., Walt, D. R., “Single cell time resolved quorum responses reveal dependence on cell density and configuration,” Journal of Biological Chemistry, vol. 286, pp. 21623-21632, 2011.

Wallace, B., Small, K., Brodley, C. E. and Trikalinos, T., “Who should label what? Instance allocation in multiple expert active learning,” Siam International Conference on Data Mining (SDM), April 2011, Phoenix, AZ, pp. 176-187.

Small, K., Wallace, B., Brodley, C. E., and Trikalinos, T., “The constrained weight-space SVM: Learning with Labeled Features,” The Twenty-eigth International Conference on Machine Learning, June 2011, Bellevue, WA, pp. 754-763.

Wallace, B., Small, K., Brodley, C. E., and Trikalinos, T. “Class imbalance, redux,” IEEE 11th International Conference on Data Mining (ICDM), December 2011, Vancouver, B.C., pp. 754763.

FRaC: A feature-modeling approach for semi-supervised and unsupervised anomaly detection
Noto, K., Slomin, D., and Brodley C. E., “FRaC: A feature-modeling approach for semi-supervised and unsupervised anomaly detection,” Data Mining and Knowledge Discovery, 25 (1), pp. 109- 133, 2011.

2010

Discovering arbitrary events types in time series
Preston, D., Protopapas, P., and Brodley, C. E., “Discovering arbitrary events types in time series,” Statistical Analysis and Data Mining, 2 (5), pp. 396-411, 2010.

Wallace, B., Small, K., Brodley, C. E., and Trikalinos, T., “Active learning for biomedical citation screening,” The Sixteenth International Conference on Knowledge Discovery and Data Mining, August 2010, Washington D.C., pp. 173-182.

Preston, P., Brodley, C. E., Khardon, R., Sulla-Menashe, D., and Friedl, M., “Redefining class definitions using constraint-based clustering: An application to remote sensing of the Earth’s surface,” The Sixteenth International Conference on Knowledge Discovery and Data Mining, August 2010, Washington D.C.

Wallace, B., Small, K., Brodley, C. E., and Trikalinos, T. “Modeling annotation time to reduce workload in comparative effectiveness reviews,” ACM International Health Informatics Symposium, November 2010, Arlington, VA, pp. 28-35.

Semi-automated screening of biomedical citations for systematic reviews
Wallace, B., Trikalinos, T., Lau, J., Brodley, C., Schmid, C. “Semi-automated screening of biomedical citations for systematic reviews,” BMC Bioinformatics, 11 (55), 2010.

2009

Finding Anomalies in Periodic Time Series
Rebbapragada, U., Protopapas, P., Brodley, C. E., and Alcock, C. “Finding Anomalies in Periodic Time Series,” Machine Learning, 74 (3), pp. 281-313, 2009.

IP Covert Channel Detection
Scabuk, C., Brodley, C. E. and Shields, C., “IP Covert Channel Detection,” ACM Transactions on Information and Systems Security (TISSEC), 12 (4), pp. 1-29, 2009.

Preston, D., Protopapas, P. and Brodley, C. E. “Event Discovery in Time Series,” SIAM International Conference on Data Mining, April 2009, Sparks, Nevada.

Rebbapragada, U., Mandrake, L., Wagstaff, K., Gleeson, D., Castao, R., Chien, S., and Brodley, C. E., “Improving onboard analysis of Hyperion images by filtering mislabled training data examples,” Proceedings of the 2009 IEEE Aerospace Conference.

2008

Wei, X., Cowen, L., Brodley, C. E., Brady, A., Sculley, D. and Slonim, D. “A distance-based method for detecting horizontal gene transfer in whole genomes,” Proceedings of the 4th International Symposium on Bioinformatics Research and Applications (ISBRA 2008), Springer Lecture Notes in Computer Science, 2008.

Rebbapragada, U., Lomasky, R., Brodley, C. E., Friedl, M. A., “Generating high-quality training data for automated land-cover mapping,” The International Geoscience and Remote Sensing Symposium, IGARSS 2008.

Preston, D., Protopapas, P., and Brodley, C. E., “Event discovery in astronomical time series,” Astronomical Data Analysis Software and Systems (ADASS) conference, 2008, Quebec City.

Rebbapragada, U., Protopapas, P., Brodley, C. E., and Alcock, C. “Anomaly detection in catalogs of periodic variable stars,” Astronomical Data Analysis Software and Systems (ADASS) conference, 2008, Quebec City.

2007

Rebbapragada, U. and Brodley, C. E., “Improving classifier accuracy by assigning confidence labels to training data,” The Eighteenth European Conference on Machine Learning, September 2007, Warsaw, Poland, pp. 708-715.

Lomasky, R., Brodley, C. E., Aerneke, M., Walt, D., and Friedl, M. “Active Class Selection, Eighteenth European Conference on Machine Learning, September 2007, Warsaw, Poland, pp. 640-647.

Lomasky, R., Brodley, C. E., Bencic, S., Aernecke, M., and Walt, D., ”Guiding class selection for an artificial nose,” NIPS Workshop: Testing of Deployable Learning and Decision Systems 2007.

2006

Sculley, D. and Brodley, C. E., “Compression and machine learning: A new perspective on feature space vectors,” Proceedings of DCC06, IEEE Data Compression Conference, March 2006, Brandeis University, pp. 332-342.

SmashGuard: A hardware solution to prevent security attacks on the function return address
Ozdoganoglu, H., Jalote, A., Vijaykumar, T. N., Brodley, C. E., and Kuperman, B. A., “SmashGuard: A hardware solution to prevent security attacks on the function return address,” IEEE Transactions on Computers, 55 (10), pp. 1271–1285, 2006.

Sculley,D., Wachman, G. and Brodley, C. E. “Spam classifiers and inexact string matching features,” TREC 2006: The Fifteenth Text Retrieval Conference Proceedings, November, 2006.

Jacob, N. and Brodley, C. E., “Offloading IDS computation to the GPU,” The Twentysecond Annual Computer Security Applications Conference, December 2006, Washington D.C., pp. 371- 380.

Computing: Report leaps geographical barriers but stumbles over gender
Pollack, M.E., et al., (one of 26 authors) “Computing: Report leaps geographical barriers but stumbles over gender,” Correspondance, Nature, vol. 44, 2006.

2005

Fern, X. Z., Brodley, C. E., and Friedl, M. A., “Correlation clustering for learning mixtures of canonical correlation models,” SIAM International Conference on Data Mining (SDM), April 2005, Newport Beach, CA, pp. 439-448.

Hasan, J., Jalote, A., Vijaykumar, T. N., and Brodley, C. E., “Heat stroke: Power-density-based denial of service in SMT,” Proceedings of the 11th International Symposium on High-Performance Computer Architecture, December 2005, Goa, India, pp. 166-177.

Detection and prevention of stack buffer overflow attacks
Kuperman, B., Brodley, C. E., Ozdoganoglu, H., Vijaykumar, T.N. and Jalote, A., “Detection and prevention of stack buffer overflow attacks,” Communications of the ACM, 48 (11), pp. 51-56, 2005.

Early, J. P. and Brodley, C. E. “Behavioral features for network anomaly detection,” in M. Maloof, (Ed.) Machine Learning and Data Mining for Computer Security: Methods and Applications, Springer, 2005, pp. 107-124.

2004

Fern, X. Z. and Brodley, C. E., “Solving cluster ensemble problems by bipartite graph partitioning,” The Twentieth-First International Conference on Machine Learning, July, 2004, Banff, Alberta.

Feature selection for unsupervised learning
Dy, J. and Brodley, C. E., “Feature selection for unsupervised learning,” Journal of Machine Learning Research, vol. 5, pp. 845–889, 2004.

Scabuk, C., Brodley, C. E., and Shields, T. C., “IP covert timing channels: An initial exploration,” The Eleventh ACM Conference on Computer and Communications Security, October, 2004, Washington D.C., pp. 176-187.

Cyber Defense Technology Networking and Evaluation
Bajcsy, R., et al. (one of 24 authors), “Cyber Defense Technology Networking and Evaluation,” Communications of the ACM, 47 (3), pp. 58-61, 2004.

2003

Unsupervised feature selection applied to content-based image retrieval of lung images
Dy, J., Brodley, C. E., Kak, A., Broderick, L. S., and Aisen, A. M., “Unsupervised feature selection applied to content-based image retrieval of lung images,” IEEE Transactions on Pattern Recognition and Machine Intelligence, vol. 25, pp. 373-378, 2003.

An empirical study of two approaches to sequence learning for anomaly detection
Lane, T. and Brodley, C. E., “An empirical study of two approaches to sequence learning for anomaly detection,” Machine Learning, 51(1), pp. 73-107, 2003.

Automated storage and retrieval of medical images to assist diagnosis: Implementation and preliminary assessment
Aisen, A. M. Broderick, L. S., Winer-Muram, H., Brodley, C. E., Kak, A. C., Pavlopoulou C., Dy, J., and Marchiori, A., “Automated storage and retrieval of medical images to assist diagnosis: Implementation and preliminary assessment,” Radiology., 228 (1), pp. 265-270, 2003.

Fern, X. Z. and Brodley, C. E., “Boosting lazy decision trees,” The Twentieth International Conference on Machine Learning, August 2003, Washington D.C., pp. 178-185.

Fern, X. Z. and Brodley, C. E., “Random Projection for high dimensional data clustering: A cluster ensemble approach,” The Twentieth International Conference on Machine Learning, August 2003, Washington D.C., pp. 186-193.

Applications of Semisupervised and Active Learning to Interactive Contour Delineation
Pavlopoulou, C., Kak, A. and Brodley, C. E., “Applications of Semisupervised and Active Learning to Interactive Contour Delineation,”, Proc. ICML-EDDS Workshop on the Continumm from Labeled to Unlabeled Data, August 2003.

User Re-authentication via Mouse Movements
Pusara, M. and Brodley, C. E., “User Re-authentication via Mouse Movements,” to appear at the Workshop on Statistical and Machine Learning Techniques in Computer Intrusion Detection, September 24-26th, 2003.

Early, J., Brodley, C. E. and Rosenberg, C., “Behavioral Authentication of Server Flows,” in the The Nineteenth Annual Computer Security Applications Conference, December 2003, Las Vegas, pp. 49-55.

2002

Using human perceptual categories for content-based retrieval from a medical image database
Shyu, C. R., Kak, A., Brodley, C. E., and Broderick, L., “Using human perceptual categories for content-based retrieval from a medical image database,” Computer Vision and Image Understanding, vol. 88, pp. 119-151, 2002.

Interactive content-based image retrieval using relevance feedback
MacArthur, S. Brodley C. E., and Broderick, L., “Interactive content-based image retrieval using relevance feedback,” Computer Vision and Image Understanding, vol. 88, pp. 55-75, 2002.

2001

The effect of instance-space partition on significance
Bradford, J. P. and Brodley, C. E., “The effect of instance-space partition on significance,” Machine Learning, 42, pp 269–286, 2001.

Focusing attention on objects of interest using multiple matched filter
Stough, T. M. and Brodley, C. E., “Focusing attention on objects of interest using multiple matched filters,” IEEE Transactions on Image Processing, 10 (3), pp. 419-426, 2001.

Marchiori, A., Brodley, C. Broderick, L., Dy, J., Pavlopoulou, C., Kak, A. and Aisen, A. ”CBIR for Medical Images – An Evaluation Trial,” in the Proceedings of the IEEE Workshop of Content- Based Access of Image and Video Databases, Hawaii, 2001.

Pavlopoulou, C., Kak, A. and Brodley, C. E., “An interactive framework for boundary delineation for medical CBIR,” in the Proceedings of the IEEE Workshop of Content-Based Access of Image and Video Databases, Hawaii, 2001.

Aisen, A. M. Broderick, L. S., Winer-Muram, H. Brodley, C. E., Kak, A. C., and Pavlopoulou C., “A Content-based Image Retrieval System for HRCT Images of the Lung: Implementation and Initial Validation,” Radiology, 2001.

2000

Relevance feedback decision trees in contentbased image retrieval
MacArthur, S. D., Brodley, C. E. and Shyu, C. R., “Relevance feedback decision trees in contentbased image retrieval,” in the Proceedings of the IEEE Workshop of Content-Based Access of Image and Video Databases, Hilton Head, SC, June 13, 2000.

Dy, J. and Brodley, C. E., “Feature subset selection and order identification for unsupervised learning,” The Seventeenth International Conference on Machine Learning, June, 2000, Stanford University, pp. 247-254.

Lane, T. and Brodley, C. E., “Data reduction techniques for instance-based learning of human/computer interface data,” The Seventeenth International Conference on Machine Learning, June, 2000, Stanford University, pp. 519-526.

Dy, J. and Brodley, C. E., “Visualization and interactive feature selection for unsupervised data,” The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August, 2000, Boston, MA, pp. 360-364.

KDD-Cup 2000: Organizers’ report
Kohavi, R., Brodley, C. E., Frasca, B., Mason, L., and Zheng, Z., “KDD-Cup 2000: Organizers’ report,” SIGKDD Explorations, 2 (2) pp. 86-98, 2000.

1999

Dy, J. G., Brodley, C. E., Kak, A. Shyu, C., and Broderick, L. S., “The customized-queries approach to CBIR,” Storage and Retrieval for Image and Video Databases VII, IS&T/SPIE Electronic Imaging 1999, vol. 3656, January, 1999, San Jose, CA, pp. 22-32.

Maximizing land cover classification accuracies produced by decision trees at continental to global scales
Friedl, M., Brodley, C. E., and Strahler, A., “Maximizing land cover classification accuracies produced by decision trees at continental to global scales,” IEEE Transactions on Geoscience and Remote Sensing, 37 (2), pp. 969-977, 1999.

ASSERT, A physician-in-the-loop content-based image retrieval system for HRCT image databases
Shyu, C., Brodley, C. E., Kak, A., Kosaka, A., Aisen, A. and Broderick, L., “ASSERT, A physician-in-the-loop content-based image retrieval system for HRCT image databases,” Computer Vision and Image Understanding, vol. 74, pp. 111-132, 1999.

Testing for human perceptual categories in a physician-in-the-loop CBIR system for medical imagery
Shyu, C. R., Kak, A., Brodley, C. E., and Broderick, L., “Testing for human perceptual categories in a physician-in-the-loop CBIR system for medical imagery,” in the Proceedings of the IEEE Workshop of Content-Based Access of Image and Video Databases, Fort Collins, CO, June 22, 1999.

Dy, J. G., Brodley, C. E., Kak, A. Shyu, C., and Broderick, L. S., “The customized-queries approach to CBIR using EM,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, June, 1999, Fort Collins, CO, pp. 400-406.

Zhou, Y. and Brodley, C. E., “A hybrid lazy-eager approach to reducing the computation and memory requirements of local parametric learning algorithms,” The Sixteenth International Conference on Machine Learning, June, 1999, Bled, Slovenia, pp. 503-512.

Brodley, C. E., Kak, A. C., Dy, J. G., Shyu, C. R., Aisen, A., and Broderick, L., “Contentbased retrieval from medical image databases: A synergy of human interaction, machine learning and computer vision,” The Sixteenth National Conference on Artificial Intelligence, July, 1999, Orlando, FL, pp. 760-767.

Kapadia, N., Fortes, J. and Brodley, C. E., “Predictive application-performance modeling in a computational grid environment,” the Eighth IEEE International Symposium on High Performance Distributed Computing, August, 1999, Redondo Beach, CA, pp. 47-54.

Identifying Mislabeled Training Data
Brodley, C. E. and Friedl, M., “Identifying mislabeled training data,” Journal of Artificial Intelligence Research, vol. 11, pp. 131-167, 1999.

Temporal Sequence Learning and Data Reduction for Anomaly Detection
Lane, T. and Brodley, C. E., “Temporal sequence learning and data reduction for anomaly detection,” ACM Transactions on Computer Security, 2 (3), pp 295–331, 1999.

Knowledge discovery and data mining
Brodley, C. E., Lane, T., and Stough, T., “Knowledge discovery and data mining,” American Scientist, 87 (1), pp. 54-61, 1999, (Invited article).

Brodley, C. E. and Clouse, J., “Machine Learning,” In the Encyclopedia of Computer Science and Technology, vol. 41 (A. Kent and J. G. Williams, eds.), Marcel Dekker, New York, 1999, pp. 137-146.

1998

Bradford, J. P., Kunz, C., Kohavi, R., Brunk, C., and Brodley, C. E., “Pruning decision trees with misclassification costs,” The Tenth European Conference on Machine Learning, April, 1998, Chemnitz, Germany, pp. 131-136.

Shyu, C., Brodley, C. E., Kak, A., Kosaka, A., Aisen, A., and Broderick, L., “Local versus global features for content-based image retrieval”, in the Proceedings of the IEEE Workshop on Content- Based Access of Image/Video Library held in conjunction with CVPR98, Santa Barbara, CA, June 21, 1998.

Lane, T. and Brodley, C. E., “Approaches to online learning and concept drift for user identification in computer security,” The Fourth International Conference on Knowledge Discovery and Data Mining, August, 1998, New York, NY, pp. 259-263.

Resource-usage prediction for demand-based network computing
Kapadia, N. H., Brodley, C. E., Fortes, J. A. B., and Lundstrom, M. S., “Resource-usage prediction for demand-based network computing,” Proceedings of the 1998 Workshop on Advances in Parallel and Distributed Systems (APADS), West Lafayette, IN, October 20, 1998, pp. 372-377.

Lane, T. and Brodley, C. E., “Temporal sequence learning and data reduction for anomaly detection” The Fifth ACM Conference on Computer and Communications Security, November, 1998, San Francisco, CA, pp. 150-158.

1997

Applying Classification Algorithms in Practice
Brodley, C. E. and Smyth, P., “Applying classification algorithms in practice,” Statistics and Computing, vol. 7, pp. 45-56, 1997.

Lane T. and Brodley C. E., “Sequence matching and learning in anomaly detection for computer security,” AAAI-97 Workshop: AI Approaches to Fraud Detection and Risk Management, Providence, RI, July 31, 1997.

Stough, T. and Brodley, C. E., “Image feature reduction through spoiling: Its application to multiple matched filters for focus of attention,” The Third International Conference on Knowledge Discovery and Data Mining, August, 1997, Newport Beach, CA, pp. 255-258.

Decision Tree Classification of Land Cover from Remotely Sensed Data
Friedl, M. and Brodley, C. E., “Decision tree classification of land cover from remotely sensed data,” Remote Sensing of Environment, 61 (3), pp. 399-409, 1997.

Lane, T. and Brodley, C. E., “An application of machine learning to anomaly detection,” The Twentieth Annual National Information Systems Security Conference, October, 1997, Washington, DC, pp. 366-380.

Moss, J. E., Utgoff, P., Cavozos, J., Precup, D., Stefanovic, D., Brodley, C. E., and Scheeff, D., “Learning to schedule straight-line code,” Neural Information Processing Systems, December 1997, Denver, CO, pp. 929-935.

1996

Sandholm, M., Sandholm, T., Brodley, C. E., and Vidovic, A., “Linear and logistic regression, symbolic induction methods, and neural networks in morbidity diagnosis and morbidity prediction in equine gastrointestinal colic,” The Second Annual SEPSIS/SIRS: Reducing Mortality to Patients & Suppliers, February 1996, Washington D.C.

Sandholm, T., Brodley, C. E., Vidovic, A., Nyholm, K., and Sandholm, M., “Comparison of regression methods, symbolic induction and methods and neural networks in morbidity diagnosis and mortality prediction in equine gastrointestinal colic,” AAAI 1996 Spring Symposium Series: Artificial Intelligence In Medicine: Applications of Current Technologies, Stanford University, Palo Alto, CA, March 25-27, 1996.

Brodley, C. E., Friedl, M. A., and Strahler, A., “New approaches to classification in remote sensing: Using hybrid decision tree and ensemble classifiers to map land cover,” Proceedings of the International Geoscience and Remote Sensing Symposium, vol. I, May 27-31, 1996, Lincoln, NE, pp. 532-534.

Brodley, C. E. and Friedl, M. A., “Improving automated land cover mapping by identifying and eliminating misclassified instances,” Proceedings of the International Geoscience and Remote Sensing Symposium, vol. II, May 27-31, 1996, Lincoln, NE, pp. 1382-1384.

Brodley, C. E. and Lane, T., “Creating and exploiting diversity,” AAAI-96 Workshop: Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms, Portland, OR, August 3, 1996.

Brodley, C. E. and Friedl, M. A., “Identifying and eliminating mislabeled training instances,” The Thirteenth National Conference on Artificial Intelligence. August 4-8, 1996, Portland, OR, pp. 799-805.

1995

Multivariate Decision Trees
Brodley, C. E. and Utgoff, P. E., “Multivariate decision trees,” Machine Learning, vol. 19, pp. 45-77, 1995.

Recursive Automatic Bias Selection for Classifier Construction
Brodley, C. E., “Recursive Automatic bias selection for classifier construction,” Machine Learning, vol. 20, pp. 63-94, 1995.

Brodley, C. E. and Smyth, P., “Applying machine learning in practice,” IMLC-95 Workshop: Applying Machine Learning in Practice, Tahoe City, CA, July 10, 1995.

Brodley, C. E., “Automatic selection of split criterion during tree growing based on node location,” The Twelfth International Conference on Machine Learning, July, 1995, Tahoe City, CA, pp. 73- 80.

1994

Goal-Directed Classification Using Linear Machine Decision Trees
Draper, B., Brodley, C. E., and Utgoff, P. E., “Goal-directed classification using linear machine decision trees,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 16 (9), pp. 888-893, 1994.

Brodley, C. E. and Utgo, P. E., “Dynamic recursive model class selection for classifier construction,” In Cheeseman and Oldford (Eds.), Selecting Models from Data, pp. 329-338, Springer-Verlag, 1994. (A preliminary version appeared in The Proceedings of the Fourth International AI and Statistics Workshop.)

1993

Dynamic recursive model class selection for classifier construction
Brodley, C. E. and Utgo, P. E., “Dynamic recursive model class selection for classifier construction,” The Fourth International AI and Statistics Workshop, Ft. Lauderdale, FL, January 1993. (Full version appeared as a book chapter).

Brodley, C. E. and Rissland, E. L., “Measuring concept changes,” AAAI Spring Symposium Series, Training Issues in Incremental Learning, Stanford University, Palo Alto, CA, March 23-25, 1993.

Brodley, C. E., “Addressing the selective superiority problem: Automatic algorithm/model class selection,” The Tenth International Conference on Machine Learning, June, 1993, Amherst, MA, pp. 17-24. (Note: one of 5% accepted for plenary session presentation.)

1990

Utgoff, P. E. and Brodley, C. E., “An incremental method for finding multivariate splits in decision trees,” The Seventh Conference on Machine Learning, June, 1990, Austin, TX, pp. 58-65.