The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Issues in Stacked Generalization
Open accessAbstract
Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a `black art' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We find that best results are obtained when the higher-level model combines the confidence (and not just the predictions) of the lower-level ones. We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms for classification tasks. We also compare the performance of stacked generalization with majority vote and published results of arcing and bagging.
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The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600
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Cited by
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Reconciling schemas of disparate data sources: a machine-learning approach
Doan, A., Domingos, P., & Halevy, A. Y. (2001) · Proceedings of the 2001 ACM SIGMOD international conference on Management of data · doi:10.1145/375663.375731 · Cited by 733
Ontology Matching: A Machine Learning Approach
Doan, A., Madhavan, J., Domingos, P., & Halevy, A. (2004) · Handbook on Ontologies · doi:10.1007/978-3-540-24750-0_19 · Cited by 477
ECG beat classifier designed by combined neural network model
Güler, İ., & Übeylı˙, E. D. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2004.06.009 · Cited by 324
Towards Automatic Recognition of Scientifically Rigorous Clinical Research Evidence
Kilicoglu, H., Demner-Fushman, D., Rindflesch, T. C., Wilczynski, N. L., & Haynes, R. B. (2009) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2996 · Cited by 101
Metadata
- Authors
- Ting, K. M., & Witten, I. H.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 1999-05-01
- Volume
- 10
- Pages
- 271-289
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W1645816215
- Cited by
- 576
- References
- 4
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- diamond
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
