Original Contribution: Stacked generalization
Neural Networks
The effect multiple query representations on information retrieval system performance
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic combination of multiple ranked retrieval systems
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Combining automatic and manual index representations in probabilistic retrieval
Journal of the American Society for Information Science
Method combination for document filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Robust Classification for Imprecise Environments
Machine Learning
A meta-learning approach for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Combining multiple classifiers for text categorization
Proceedings of the tenth international conference on Information and knowledge management
Information Retrieval
Maximizing Text-Mining Performance
IEEE Intelligent Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Combining Multiple Learning Strategies for Effective Cross Validation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Issues in stacked generalization
Journal of Artificial Intelligence Research
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Using bayesian priors to combine classifiers for adaptive filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
GE-CKO: A Method to Optimize Composite Kernels for Web Page Classification
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
The Combination of Text Classifiers Using Reliability Indicators
Information Retrieval
Actions, answers, and uncertainty: a decision-making perspective on Web-based question answering
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
An analysis of the relative hardness of Reuters-21578 subsets: Research Articles
Journal of the American Society for Information Science and Technology
Methods for learning classifier combinations: no clear winner
Proceedings of the 2005 ACM symposium on Applied computing
Local sparsity control for naive Bayes with extreme misclassification costs
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Table Recognition Evaluation and Combination Methods
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Combining classifiers to identify online databases
Proceedings of the 16th international conference on World Wide Web
An Effective Evidence Theory Based K-Nearest Neighbor (KNN) Classification
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Genre-based decomposition of email class noise
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Hierarchical Ensemble Support Cluster Machine
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Confidence estimation for information extraction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Using text classification and multiple concepts to answer e-mails
Expert Systems with Applications: An International Journal
Using Kullback-Leibler distance for text categorization
ECIR'03 Proceedings of the 25th European conference on IR research
Combining bi-gram of character and word to classify two-class chinese texts in two steps
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Classifying chinese texts in two steps
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Automatic categorization of patent applications using classifier combinations
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Toward file consolidation by document categorization
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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The intuition that different text classifiers behave in qualitatively different ways has long motivated attempts to build a better metaclassifier via some combination of classifiers. We introduce a probabilistic method for combining classifiers that considers the context-sensitive reliabilities of contributing classifiers. The method harnesses reliability indicators---variables that provide a valuable signal about the performance of classifiers in different situations. We provide background, present procedures for building metaclassifiers that take into consideration both reliability indicators and classifier outputs, and review a set of comparative studies undertaken to evaluate the methodology.