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Combination of multiple classifiers using local accuracy estimates
Abstract
This paper presents a method for combining classifiers that uses estimates of each individual classifier's local accuracy in small regions of feature space surrounding an unknown test sample. An empirical evaluation using five real data sets confirms the validity of our approach compared to some other combination of multiple classifiers algorithms. We also suggest a methodology for determining the best mix of individual classifiers.
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Citations
Cited by
Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657
A Completed Modeling of Local Binary Pattern Operator for Texture Classification
Guo, Z., Zhang, L., & Zhang, D. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2044957 · Cited by 2,115
Mixture of experts: a literature survey
Masoudnia, S., & Ebrahimpour, R. (2014) · Artificial Intelligence Review · doi:10.1007/s10462-012-9338-y · Cited by 561
Series in Machine Perception and Artificial Intelligence
(2012) · Series in machine perception and artificial intelligence · doi:10.1142/smpai · Cited by 365
Ensembles of Learning Machines
Valentini, G., & Masulli, F. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45808-5_1 · Cited by 276
Metadata
- Authors
- Woods, K., Kegelmeyer, W. P., & Bowyer, K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1997-04
- Volume
- 19
- Issue
- 4
- Pages
- 405-410
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2098154993
- Cited by
- 952
- References
- 0
- FWCI
- 19.02
- Type
- journal article
- Language
- en
- Open access
- closed
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Combination of multiple classifiers using local accuracy estimates. https://app.cochat.ai/papers/10.1109/34.588027
- The paper
- Woods, K., Kegelmeyer, W. P., & Bowyer, K. (1997). Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.588027
Data sources
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.
