C4.5: programs for machine learning
C4.5: programs for machine learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
WWW-based negotiation support: design, implementation, and use
Decision Support Systems
Machine Learning - Special issue on multistrategy learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Conflict resolution in collaborative planning dialogs
International Journal of Human-Computer Studies - Special issue on collaboration, cooperation and conflict in dialogue systems
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Artificial Intelligence
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Bilateral Negotiation Decisions with Uncertain Dynamic Outside Options
WEC '04 Proceedings of the First IEEE International Workshop on Electronic Contracting
A selective sampling approach to active feature selection
Artificial Intelligence
Randomized Variable Elimination
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data Mining
Feature generation for text categorization using world knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Analysis and classification of strategies in electronic negotiations
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Sample compression bounds for decision trees
Proceedings of the 24th international conference on Machine learning
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We introduce Process-Specific Feature Selection, an innovative procedure of feature selection for textual data. The procedure applies to data gathered in person-to-person communication. The procedure relies on the knowledge of the processes that govern such communication. It is general enough to represent data in a wide variety of domains. We present a case study of electronic negotiation, in which participants exchange text messages. We present the empirical results of classifying the outcomes of electronic negotiations based on such texts. The results achieved using process-specific feature selection are marginally better than those afforded by several traditional feature selection methods. We show that this tendency is consistent across several learning paradigms.