Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Error reduction through learning multiple descriptions
Machine Learning
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning for Information Extraction in Informal Domains
Machine Learning - Special issue on information retrieval
IEPAD: information extraction based on pattern discovery
Proceedings of the 10th international conference on World Wide Web
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A flexible learning system for wrapping tables and lists in HTML documents
Proceedings of the 11th international conference on World Wide Web
Hierarchical Wrapper Induction for Semistructured Information Sources
Autonomous Agents and Multi-Agent Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active Learning for Natural Language Parsing and Information Extraction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
RoadRunner: Towards Automatic Data Extraction from Large Web Sites
Proceedings of the 27th International Conference on Very Large Data Bases
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Extraction Techniques for Mining Services from Web Sources
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An extensible meta-learning approach for scalable and accurate inductive learning
An extensible meta-learning approach for scalable and accurate inductive learning
Wrapper induction for information extraction
Wrapper induction for information extraction
Bottom-up relational learning of pattern matching rules for information extraction
The Journal of Machine Learning Research
Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
Combining Classifiers for word sense disambiguation
Natural Language Engineering
Sources of Success for Boosted Wrapper Induction
The Journal of Machine Learning Research
Issues in stacked generalization
Journal of Artificial Intelligence Research
Adaptive information extraction from text by rule induction and generalisation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Data & Knowledge Engineering
Algorithms for the coalitional manipulation problem
Artificial Intelligence
Edge Detection from Global and Local Views Using an Ensemble of Multiple Edge Detectors
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Exploiting context analysis for combining multiple entity resolution systems
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Four Heads Are Better than One: Combining Suggestions for Case Adaptation
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Voting almost maximizes social welfare despite limited communication
Artificial Intelligence
A case study of stacked multi-view learning in dementia research
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Grammatical inference in practice: a case study in the biomedical domain
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
A machine learning solution to assess privacy policy completeness: (short paper)
Proceedings of the 2012 ACM workshop on Privacy in the electronic society
Ensemble learning for sentiment classification
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
A comparative study of classifier combination applied to NLP tasks
Information Fusion
Hi-index | 0.01 |
This article investigates the effectiveness of voting and stacked generalization -also known as stacking- in the context of information extraction (IE). A new stacking framework is proposed that accommodates well-known approaches for IE. The key idea is to perform cross-validation on the base-level data set, which consists of text documents annotated with relevant information, in order to create a meta-level data set that consists of feature vectors. A classifier is then trained using the new vectors. Therefore, base-level IE systems are combined with a common classifier at the meta-level. Various voting schemes are presented for comparing against stacking in various IE domains. Well known IE systems are employed at the base-level, together with a variety of classifiers at the meta-level. Results show that both voting and stacking work better when relying on probabilistic estimates by the base-level systems. Voting proved to be effective in most domains in the experiments. Stacking, on the other hand, proved to be consistently effective over all domains, doing comparably or better than voting and always better than the best base-level systems. Particular emphasis is also given to explaining the results obtained by voting and stacking at the meta-level, with respect to the varying degree of similarity in the output of the base-level systems.