The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Decision combination in multiple classifier systems
Abstract
A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings of classifiers and different instances of a problem. The rankings can be combined by methods that either reduce or rerank a given set of classes. An intersection method and union method are proposed for class set reduction. Three methods based on the highest rank, the Borda count, and logistic regression are proposed for class set reranking. These methods have been tested in applications of degraded machine-printed characters and works from large lexicons, resulting in substantial improvement in overall correctness.>
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013) · Wiley Series in Probability and Statistics · doi:10.1002/9781118548387 · Cited by 13,861
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Cited by
An introduction to biometric recognition
Jain, A. K., Ross, A., & Prabhakar, S. (2004) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2003.818349 · Cited by 4,974
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072
A tutorial on human activity recognition using body-worn inertial sensors
Bulling, A., Blanke, U., & Schiele, B. (2014) · ACM Computing Surveys · doi:10.1145/2499621 · Cited by 1,668
Information fusion in biometrics
Ross, A., & Jain, A. (2003) · Pattern Recognition Letters · doi:10.1016/s0167-8655(03)00079-5 · Cited by 1,107
Ensemble learning for data stream analysis: A survey
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.004 · Cited by 1,050
Multimodal Biometrics: An Overview
Ross, A., & Jain, A. (2004) · Zenodo · doi:10.5281/zenodo.38715 · Cited by 593
Ensemble of feature sets and classification algorithms for sentiment classification
Xia, R., Zong, C., & Li, S. (2011) · Information Sciences · doi:10.1016/j.ins.2010.11.023 · Cited by 583
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
Ward, J. A., Lukowicz, P., Troster, G., & Starner, T. E. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.197 · Cited by 431
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
Recognition of dietary activity events using on-body sensors
Amft, O., & Tröster, G. (2008) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2007.11.007 · Cited by 220
Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness
Zappi, P., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., & Troster, G. (2007) · 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information · doi:10.1109/issnip.2007.4496857 · Cited by 126
Son, H., Kim, C., Hwang, N., Kim, C., & Kang, Y. (2014) · Advanced Engineering Informatics · doi:10.1016/j.aei.2013.10.001 · Cited by 74
Ensemble approach for time series analysis in demand forecasting: Ensemble learning
Akyuz, A. O., Uysal, M., Bulbul, B. A., & Uysal, M. O. (2017) · 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) · doi:10.1109/inista.2017.8001123 · Cited by 38
Das, S. K., Mudi, R., Rahman, M. S., Rabie, K. M., & Li, X. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3466858 · Cited by 27
Modeling Uncertainty in Multi-Modal Fusion for Lung Cancer Survival Analysis
Wang, H., Subramanian, V., & Syeda-Mahmood, T. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433823 · Cited by 26
Metadata
- Authors
- Ho, T. K., Hull, J. J., & Srihari, S. N.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1994
- Volume
- 16
- Issue
- 1
- Pages
- 66-75
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2102734279
- Cited by
- 1,511
- References
- 3
- FWCI
- 21.90
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.
