Publications

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2010

Maloof, M. A. (2010, in press). In Koronacki, J., Wirzchon, S.T., Ras, Z., and Kacprzyk, J., eds., Recent Advances in Machine Learning: Dedicated to the Memory of Ryszard S. Michalski. Berlin-Heidelburg: Springer.

2009

Caputo, D. D., Maloof, M. A., and Stephens, G. D. (2009). Detecting insider theft of trade secrets. IEEE Security & Privacy, November/December, 7.6:18–25.

Maloof, M.A. (2009). On the performance of online learning methods for detecting malicious executables. In Tsai, J., and Yu, P., eds., Machine Learning in Cyber Trust—Security, Reliability, and Privacy, 109–132. Berlin-Heidelburg: Springer.

2008

Bach, S.H., & Maloof, M.A. (2008). Paired learners for concept drift. Proceedings of the Eighth IEEE International Conference on Data Mining, 23–32. Los Alamitos, CA: IEEE Press.

2007

Kolter, J.Z., & Maloof, M.A. (2007). Dynamic Weighted Majority: An ensemble method for drifting concepts. Journal of Machine Learning Research 8:2755–2790.

Maloof, M.A., & Stephens, G.D. (2007). ELICIT: A system for detecting insiders who violate need-to-know. Recent Advances in Intrusion Detection, 146–166. Lecture Notes in Computer Science, Volume 4637. Berlin: Springer.

2006

Kolter, J.Z., & Maloof, M.A. (2006). Learning to detect and classify malicious executables in the wild. Journal of Machine Learning Research 7:2721–2744. (Special Issue on Machine Learning in Computer Security)

Maloof, M.A., Ed. (2006). Machine learning and data mining for computer security: Methods and applications. London: Springer. Amazon, Barnes & Noble, Reiters, Google Books.

2005

Kolter, J.Z., & Maloof, M.A. (2005). Using additive expert ensembles to cope with concept drift. In Proceedings of the Twenty-second International Conference on Machine Learning, 449–456. New York, NY: ACM Press.

2004

Kolter, J.Z., & Maloof, M.A. (2004). Learning to detect malicious executables in the wild. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 470–478. New York, NY: ACM Press. (Best Application Paper)

Maloof, M.A., & Michalski, R.S. (2004). Incremental learning with partial instance memory. Artificial Intelligence 154:95–126.

2003

Kolter, J.Z., & Maloof, M.A. (2003). Dynamic Weighted Majority: A new ensemble method for tracking concept drift. Proceedings of the Third International IEEE Conference on Data Mining, 123–130. Los Alamitos, CA: IEEE Press.

Beiden, S.V., Maloof, M.A., & Wagner, R.F. (2003). A general model for finite-sample effects in training and testing of competing classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 25:1561–1569.

Maloof, M.A., Langley, P., Binford, T.O., Nevatia, R., & Sage, S. (2003). Improved rooftop detection in aerial images with machine learning. Machine Learning 53:157–191.

Maloof, M.A. (2003). Learning when data sets are imbalanced and when costs are unequal and unknown. ICML-2003 Workshop on Learning from Imbalanced Data Sets II.

Maloof, M.A. (2003). Incremental rule learning with partial instance memory for changing concepts. Proceedings of the International Joint Conference on Neural Networks, 2764–2769. Los Alamitos, CA: IEEE Press.

Kolter, J.Z., & Maloof, M.A. (2003). Dynamic Weighted Majority: A new ensemble method for tracking concept drift. Technical Report CSTR-20030610-3. Department of Computer Science, Georgetown University, Washington, DC.

2002

Maloof, M.A. (2002). On machine learning, ROC analysis, and statistical tests of significance. Proceedings of the Sixteenth International Conference on Pattern Recognition, 204–207, Los Alamitos, CA: IEEE Press.

Maloof, M.A., & Michalski, R.S. (2002). Incremental learning with partial instance memory. Foundations of intelligent systems, Lecture Notes in Artificial Intelligence, Vol. 2366, 16–27. Berlin: Springer-Verlag. (Proceedings of the Thirteenth International Symposium on Methodologies for Intelligent Systems, Lyon, France, June 29–29)

Beiden, S.V., Maloof, M.A., & Wagner, R.F. (2002). Analysis of competing classifiers in terms of components of variance of ROC summary accuracy measures: Generalization to a population of trainers and a population of testers. Proceedings of the SPIE International Symposium on Medical Imaging: Image Processing, Vol. 4684.

Maloof, M.A., Beiden, S.V., & Wagner, R.F. (2002). Analysis of competing classifiers using components of variance of ROC accuracy measures. Technical Report CS-02-01. Department of Computer Science, Georgetown University, Washington, DC.

2000

Maloof, M.A. & Michalski, R.S. (2000). Selecting examples for partial memory learning. Machine Learning, 41:27–52.

Maloof, M.A. (2000). An initial study of an adaptive hierarchical vision system. Proceedings of the Seventeenth International Conference on Machine Learning, 567–573. San Francisco, CA: Morgan Kaufmann

Maloof, M.A. (2000). An adaptive architecture for hierarchical vision systems. Adaptive User Interfaces: Papers from the 2000 AAAI Spring Symposium, 74–79. Technical Report SS-00-01. Menlo Park, CA: AAAI Press.

1999

Maloof, M.A. (1999). A machine learning researcher's foray into recidivism prediction. Technical Report CS-99-02. Department of Computer Science, Georgetown University, Washington, DC.

Maloof, M.A. (1999). Design of a machine learning architecture for hierarchical vision systems. Technical Report CS-99-01. Department of Computer Science, Georgetown University, Washington, DC.

Maloof, M.A. & Michalski, R.S. (1999). AQ-PM: A system for partial memory learning. Proceedings of the Eighth Workshop on Intelligent Information Systems, 70–79. Warsaw, Poland: Polish Academy of Sciences.

1998

Ali, K.M., Langley, P., Maloof, M.A., Sage, S., & Binford, T.O. (1998). Improving rooftop detection with interactive visual learning. Proceedings of the Image Understanding Workshop, 479–492. San Francisco, CA: Morgan Kaufmann.

Maloof, M.A., Langley, P., Binford, T.O., & Nevatia, R. (1998). Generalizing over aspect and location for rooftop detection. Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision (WACV '98), 194–199.

Maloof, M.A., Langley, P., Binford, T.O., & Sage, S. (1998). Improving rooftop detection in aerial images through machine learning. Technical Report 98-1, Institute for the Study of Learning and Expertise, Palo Alto, CA.

Michalski, R.S., Rosenfeld, A., Duric, Z., Maloof, M.A., & Zhang, Q. (1998). Learning patterns in images. In Michalski, R.S., Bratko, I., & Kubat, M., eds., Machine Learning and Data Mining: Methods and Applications, 241–268. New York, NY: John Wiley & Sons.

1997

Maloof, M.A., Langley, P., Sage, S., & Binford, T.O. (1997). Learning to detect rooftops in aerial images. Proceedings of the Image Understanding Workshop, 835–845. San Francisco, CA: Morgan Kaufmann.

Maloof, M.A., & Michalski, R.S. (1997). Learning symbolic descriptions of shape for object recognition in X-ray images. Expert Systems with Applications 12.1:11–20.

1996

Maloof, M.A. (1996). Progressive partial memory learning. Ph.D. Dissertation, George Mason University, Fairfax, VA.

Maloof, M.A., Duric, Z., Michalski, R.S., & Rosenfeld, A. (1996). Recognizing blasting caps in X-ray images. Proceedings of the Image Understanding Workshop, 1257–1261. San Francisco, CA: Morgan Kaufmann.

Michalski, R.S., Zhang, Q., Maloof, M.A., & Bloedorn, E. (1996). The MIST methodology and its application to natural scene interpretation. Proceedings of the Image Understanding Workshop, 1473–1479. San Francisco, CA: Morgan Kaufmann.

Michalski, R.S., Rosenfeld, A., Aloimonos, Y., Duric, Z., Maloof, M.A., & Zhang, Q. (1996). Progress on vision through learning: A collaborative effort of George Mason University and University of Maryland. Proceedings of the Image Understanding Workshop, 177–187. San Francisco, CA: Morgan Kaufmann.

1995

Maloof, M.A., & Michalski, R.S. (1995). Learning evolving concepts using a partial memory approach. Working Notes of the AAAI Fall Symposium on Active Learning, 70–73. Menlo Park, CA: AAAI Press.

Maloof, M.A., & Michalski, R.S. (1995). A method for partial-memory incremental learning and its application to computer intrusion detection. Proceedings of the Seventh IEEE International Conference on Tools with Artificial Intelligence, 392–397.

Maloof, M.A., & Michalski, R.S. (1995). Learning descriptions of 2D shapes for object recognition in X-ray images. Proceedings of the Eighth International Symposium on Artificial Intelligence, 124–131.

Maloof, M.A., & Michalski, R.S. (1995). A partial memory incremental learning methodology and its application to computer intrusion detection. Reports of the Machine Learning and Inference Laboratory, MLI 95-2. Machine Learning and Inference Laboratory, Department of Computer Science, George Mason University, Fairfax, VA.

Ancient History

Maloof, M.A., & Michalski, R.S. (1994). Learning descriptions of 2D blob-like shapes for object recognition in X-ray images: an initial study. Reports of the Machine Learning and Inference Laboratory, MLI 94-4. Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA.

Nguyen, Q.-A., & Maloof, M.A. (1993). Protocol verification using relational algebra. Proceedings of the 23rd Annual Virginia Computer User's Conference, 47–51. Blacksburg, VA: Department of Computer Science, Virginia Tech.

Maloof, M.A. (1992). TPS: Incorporating temporal reasoning into a production system. Master's Thesis, University of Georgia, Athens.