Welcome. I am a PhD student at the University of Texas at Austin whose
research is focused on data mining. In particular, I am interested in
utility based data mining (particularly cost sensitive learning, active
learning, and mining when cases are rare (outlier detection and
imbalanced datasets)), text mining, and data mining in the presence of
large amounts of noise.
I can be contacted at: ayliu [at] mail.utexas.edu
resume
Below are some of my publications in reverse chronological order.
Publications (in reverse chronological order)
A. Liu, G. Jun and J. Ghosh. "A Self-Training Approach to Cost Sensitive Uncertainty Sampling", in "Machine Learning Journal", Volume 76, Numbers 2-3, September, 2009, pp. 257-270.
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A. Liu, C. Martin, B. La Cour, J. Ghosh. "Effects of Oversampling versus Cost-sensitive Learning for Bayesian and SVM Classifiers." To appear in Annals of Information Systems Special Issue on Data Mining, 2009.
A. Liu, G. Jun, and J. Ghosh. "Active learning of hyperspectral data with spatially dependent label acquisition costs." Proc. IEEE International Geoscience and Remote Sensing Symposium, 2009.
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J. Ghosh, A. Liu. "The k-means algorithm." in "The Top-Ten Algorithms in Data Mining.", X. Wu and V. Kumar (Eds), Chapman & Hall/CRC Press, 2009, pp. 21-36.
A. Liu, G. Jun, J. Ghosh. "Spatially Cost-sensitive Active Learning." In SIAM International Conference on Data Mining (SDM), 2009.
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A. Liu, G. Jun, J. Ghosh. "Active Learning with Spatially Sensitive Labeling Costs." In NIPS Workshop on Cost-sensitive Learning, December 2008.
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G. Gupta, A. Liu, J. Ghosh. "Automated Hierarchical Density Shaving: A robust, automated clustering and visualization framework for large biological datasets." In IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11 Mar 2008, IEEE Computer Society Digital Library.
Link to TCBB
C. K. Chung, C. Jones, A. Liu, J. W. Pennebaker. "Predicting Success and Failure in Weight Loss Blogs through Natural Language Use." In International Conference on Weblogs and Social Media (ICWSM), April 2008.
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A. Liu, J. Ghosh, and C. Martin. "A framework for analyzing skew in evaluation metrics." In AAAI 2007 Workshop on Evaluation Methods for Machine Learning II, July 2007.
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A. Liu, J. Ghosh, and C. Martin. "Generative oversampling for imbalanced datasets." In International Conference on Data Mining (DMIN), June 2007.
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Received "Best student paper" award
G. Gupta, A. Liu, and J. Ghosh. "Hierarchical Density Shaving: A clustering and visualization framework for large biological datasets." In ICDM Workshop on Data Mining in Bioinformatics, December 2006.
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G. Gupta, A. Liu, and J. Ghosh. "Clustering and Visualization of High-Dimensional Biological Datasets using a fast HMA Approximation." In ANNIE 2006, November 2006.
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B. Gu, P. Konana, A. Liu, B. Rajagopalan, and J. Ghosh. "Identifying Information in Stock Message Boards and Its Implications for Stock Market Efficiency." In Workshop on Information Systems and Economics (WISE), December 2006.
Link to WISE 2006
B. Gu, P. Konana, A. Liu, B. Rajagopalan, and J. Ghosh. "Predictive Value of Stock Message Board Sentiments." McCombs Research Paper No. IROM-11-06, November 2006.
Link to SSRN
G. Gupta, A. Liu, and J. Ghosh. "Automated Hierarchical Density Shaving and Gene DIVER", IDEAL Tech Report No. IDEAL-2006-TR05, September 2006.
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A. Liu, C. Martin, T. Hetherington, and S. Matzner. "AI Lessons Learned from Experiments in Insider Threat Detection." In AAAI Spring Symposium, March 2006.
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A. Liu, C. Martin, T. Hetherington, and S. Matzner. "A Comparison of System Call Feature Representations for Insider Threat Detection." In IEEE Information Assurance Workshop, June 2005.
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