A comparison of two methods for Boolean query relevancy feedback
Information Processing and Management: an International Journal
Advanced feedback methods in information retrieval
Journal of the American Society for Information Science
Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Optimum polynomial retrieval functions based on the probability ranking principle
ACM Transactions on Information Systems (TOIS)
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
C4.5: programs for machine learning
C4.5: programs for machine learning
Using induction to refine information retrieval strategies
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Information Processing and Management: an International Journal
A classification approach to Boolean query reformulation
Journal of the American Society for Information Science
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval
Modern Information Retrieval
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Identifying user preferences with Wrapper-based Decision Trees
Expert Systems with Applications: An International Journal
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The Decision Tree Forest (DTF) is an architecture for information retrieval that uses a separate decision tree for each document in a collection. Experiments were conducted in which DTFs working with the incremental tree induction (ITI) algorithm of Utgoff, Berkman, and Clouse (1997) were trained and evaluated in the medical and word processing domains using the Cystic Fibrosis and SIFT collections. Performance was compared with that of a conventional inverted index system (IIS) using a BM25-derived probabilistic matching function. Initial results using DTF were poor compared to those obtained with IIS. We then simulated scenarios in which large quantities of training data were available, by using only those parts of the document collection that were well covered by the data sets. Consequently the retrieval effectiveness of DTF improved substantially. In one particular experiment precision and recall for DTF were 0.65 and 0.67 respectively, values that compared favorably with values of 0.49 and 0.56 for IIS. © 2006 Wiley Periodicals, Inc.