Prem Melville

    [Publications | Committees | Patents | Contact]

    pmelvil@us.ibm.com

I am a Research Scientist in the Predictive Modeling Group at IBM T.J. Watson Research Center. My research interests lie in Machine Learning and Data Mining; more specifically, I am interested in active learning, ensemble methods, active feature-value acquisition, recommender systems, class probability estimation, semi-supervised learning, text classification, and applications of data mining to analyzing blogs, business analytics and e-commerce.

I got my Ph.D. from the Department of Computer Sciences at the University of Texas at Austin. At UT, I was a member of the Machine Learning group, led by Raymond Mooney. Prior to that I was at Brandeis University, where I worked at the DEMO Lab, Interaction Lab, and the Vision Lab.

• Customer Targeting Models Using Actively-Selected Web Content. [PDF]
  Prem Melville, Saharon Rosset, and Richard Lawrence.
  In Proceedings of 14h Conference on Knowledge Discovery and Data Mining (KDD-08), Las Vegas, August 24-27, 2008.
  
  
• Using Predictive Analysis to Improve Invoice-to-Cash Collection. [PDF]
  Sai Zeng, Prem Melville, Christian Lang, Ioana Boier-Martin, and Conrad Murphy.
  In Proceedings of 14h Conference on Knowledge Discovery and Data Mining (KDD-08), Las Vegas, August 24-27, 2008.
  
  
• Winner’s Report: KDD CUP Breast Cancer Identification. [PDF]
  Claudia Perlich, Prem Melville, Yan Liu, Saharon Rosset, Grzegorz Swirszcz, and Richard Lawrence.
  In Proceedings of the KDD-08 Workshop on Mining Medical Data, Las Vegas, August 24-27, 2008.
  
  

• Active Feature-value Acquisition. [PDF]
  Maytal Saar-Tsechansky, Prem Melville, and Foster Provost.
  Management Science, 2008.
  
  
• Incorporating Background Knowledge into Text Categorization for Improved Sentiment Analysis. [PDF]
  Prem Melville, Wojciech Gryc, and Richard Lawrence.
  Technical Report, 2008.
  
  
• Integrating Data Modeling and Dynamic Optimization using Constrained
  Reinforcement Learning. [PDF]
  Naoki Abe, Prem Melville, Chandan K. Reddy, Cezar Pendus and David L. Jensen.
  Technical Report, 2008.
  
  
• Learning Blog Sentiment with Reduced Supervision. [PDF]
  Prem Melville, Yan Liu, Wojciech Gryc, Richard Lawrence, Claudia Perlich.
  Technical Report, 2008.
  
  
• Cost-Effective Clustering through Active Feature-value Acquisition. [PDF]
  Duy Vu, Prem Melville, Mikhail Bilenko, and Maytal Saar-Tsechansky.
  Technical Report, 2008.
  
  
• A Machine-Learning Approach to Discovering Company Home Pages. [PDF]
  Wojciech Gryc, Prem Melville, and Richard Lawrence.
  Technical Report, 2008.
  
  
• Data Acquisition and Cost-Effective Predictive Modeling: Targeting Offers for Electronic Commerce. [PDF]
  Foster Provost, Prem Melville, and Maytal Saar-Tsechansky.
  In Proceedings of the Ninth International Conference on Electronic Commerce, 2007.
  
  
• Finding New Customers using Unstructured and Structured Data. [PDF]
  Prem Melville, Yan Liu, Richard Lawrence, Ildar Khabibrakhmanov, Cezar Pendus, and Timothy Bowden.
  In Proceedings of the KDD-07 Workshop on Mining Multiple Information Sources, 2007.
  
  
• Intelligent Information Acquisition for Improved Clustering. [PDF]
  Duy Vu, Prem Melville, Mikhail Bilenko, and Maytal Saar-Tsechansky.
  In Workshop on Information Technologies and Systems (WITS), 2007.
  
  
• Predictive Modeling for Collections of Accounts Receivable. [PDF]
  Sai Zeng, Prem Melville, Christian Lang, Ioana Boier-Martin, and Conrad Murphy.
  In Proceedings of the KDD-07 Workshop Domain Driven Data Mining, 2007.
  
  
• An Expected Utility Approach to Active Feature-value Acquisition. [PDF]
  Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond Mooney.
  In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM-05), 2005.
  
  
• Economical Active Feature-value Acquisition through Expected Utility Estimation[Abstract] [Gzipped PS] [PDF]
  Melville, P., Saar-Tsechansky, M., Provost, F. and Mooney, R.J.
  Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, Chicago, IL, August 2005.
  
  
• Active Learning for Probability Estimation using Jensen-Shannon Divergence [Abstract] [Gzipped PS] [PDF]
  Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, and Raymond J. Mooney
  In Proceedings of The 16th European Conference on Machine Learning (ECML), Porto, Portugal, 2005.
  
  
• Combining Bias and Variance Reduction Techniques for Regression [Abstract] [Gzipped PS] [PDF]
  Yuk Lai Suen, Prem Melville and Raymond J. Mooney
  In Proceedings of The 16th European Conference on Machine Learning (ECML), Porto, Portugal, 2005
  
  
• Active Feature Acquisition for Classifier Induction [Abstract] [Gzipped PS] [PDF] [Tech Report]
  Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney
  In Proceedings of the Fourth International Conference on Data Mining (ICDM-2004). Brighton, UK. November 2004.
  Also appears as Technical Report UT-AI-TR-04-311, Artificial Intelligence Lab, University of Texas at Austin, Feb 2004.
  
  
• Diverse Ensembles for Active Learning [Abstract] [Gzipped PS] [PDF][Tech Report]
  Prem Melville and Raymond J. Mooney
  Proceedings of the 21st International Conference on Machine Learning (ICML-2004), pp. 584-591, Banff, Canada, July 2004.
  
  
• Experiments on Ensembles with Missing and Noisy Data[Abstract] [Gzipped PS] [PDF]
  Prem Melville, Nishit Shah, Lilyana Mihalkova, and Raymond J. Mooney
  Proceedings of the Fifth International Workshop on Multiple Classifier Systems (MCS-2004), F. Roli, J. Kittler, and T. Windeatt (Eds.), Lecture Notes in Computer Science, Vol. 3077, pp. 293-302, Cagliari, Italy, Springer Verlag, June 2004.
  
  
• Creating Diversity in Ensembles Using Artificial Data [Abstract] [Gzipped PS] [PDF]
  Prem Melville and Raymond J. Mooney
  Information Fusion: Special Issue on Diversity in Multiclassifier Systems, 2004.
  
  
• Relational Data Mining with Inductive Logic Programming for Link Discovery [Abstract] [Gzipped PS] [PDF]
  Raymond J. Moonney, Prem Melville, Lappoon R. Tang, Jude Shavlik, Inês de Castro Dutra, and David Page
  Data Mining: Next Generation Challenges and Future Directions, H. Kargupta, A. Joshi, K. Srivakumar, and Y. Yesha (Eds.), AAAI Press, 2004.
  
  
• Creating Diverse Ensemble Classifiers [Abstract] [Gzipped PS] [PDF]
  Prem Melville
  Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, Oct 2003.
  Also appears as Technical Report UT-AI-TR-03-306, Artificial Intelligence Lab, University of Texas at Austin, December 2003.
  
  
• Constructing Diverse Classifier Ensembles Using Artificial Training Examples [Abstract] [Gzipped PS] [PDF]
  Prem Melville and Raymond J. Mooney
  Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), pp. 505-510, Acapulco, Mexico, August, 2003.
  
  
• Scaling up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism [Abstract] [Gzipped PS] [PDF]
  Lappoon R. Tang, Raymond J. Mooney and Prem Melville
  Proceedings of the KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), Washington DC, August 2003.
  
  
• Relational Data Mining with Inductive Logic Programming for Link Discovery [Abstract] [Gzipped PS] [PDF]
  Raymond J. Mooney, Prem Melville, Lappoon R. Tang, Jude Shavlik, Inês de Castro Dutra, David Page and Vítor Santos Costa
  Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, MD, November 2002.
  (Workshop version of the book chapter.)
  
  
• Content-Boosted Collaborative Filtering for Improved Recommendations [Abstract] [Gzipped PS] [PDF]
  Prem Melville, Raymond J. Mooney and Ramadass Nagarajan
  Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002), pp. 187-192, Edmonton, Canada, July 2002.
  
  
• Content-Boosted Collaborative Filtering [Abstract] [Gzipped PS] [PDF]
  Prem Melville, Raymond J. Mooney and Ramadass Nagarajan
  Proceedings of the SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA, September 2001. (Workshop version of the above.)
  
  
• Natural language assistant: A Dialog System for Online Product Recommendation
  J. Chai, V. Horvath, N. Nicolov, M. Stys, N. Kambhatla, W. Zadrozny, and P. Melville AI Magazine, 23(2):63--76, 2002.

• 23rd Conference on Artificial Intelligence, Chicago, IL, July 13-17, 2008 (AAAI 2008).

• IEEE International Conference on Data Mining, Pisa, Italy, December 15-19, 2008 (ICDM 2008).
  
• European Conference on Machine Learning, Antwerp, Belgium, September 15-19, 2008 (ECML 2008).
• 17th Conference on Information and Knowledge Management, Napa Valley, California
  October 26-30, 2008 (CIKM 2008).
• KDD Workshop on Mining Multiple Information Sources, Las Vegas, Nevada, August 24, 2008 (MMIS 2008). 
  
• Thirteenth Conference on Knowledge Discovery and Data Mining, San Jose, August 12-15, 2007 (KDD 2007).
• 22nd Conference on Artificial Intelligence, Vancouver, Canada, July 22-26, 2007 (AAAI 2007).
• Eighteenth European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007 (ECML 2007).
• Twelfth Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, August 20-23, 2006 (KDD 2006).
• 23rd International Conference on Machine Learning, Pittsburgh, PA, June 25-29, 2006 (ICML 2006).
• 21st National Conference on Artificial Intelligence, Boston, MA, July 16-20, 2006, (AAAI 2006).
• Seventeenth European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006 (ECML 2006).
• Second KDD Workshop on Utility-Based Data Mining, Philadelphia, PA, August 20, 2006 (UBDM 2006).
• Sixteenth European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, (ECML 2005).
• First KDD Workshop on Utility-Based Data Mining, Chicago, IL, August 21, 2005 (UBDM 2005).
  

IBM T.J. Watson Research Center

1101 Kitchawan Rd, Route 134/PO Box 218

Yorktown Heights, NY 10598

pmelvil@us.ibm.com