Consensus theoretic classification methods
Consensus theory is adopted as a means of classifying geographic data from multiple sources. The foundations and usefulness of different consensus theoretic methods are discussed in conjunction with pattern recognition. Weight selections for different data sources are considered and modeling of non-...
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| Veröffentlicht in: | IEEE transactions on systems, man, and cybernetics man, and cybernetics, 1992-07, Vol.22 (4), p.688-704 |
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| container_end_page | 704 |
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| container_title | IEEE transactions on systems, man, and cybernetics |
| container_volume | 22 |
| creator | Benediktsson, J.A. Swain, P.H. |
| description | Consensus theory is adopted as a means of classifying geographic data from multiple sources. The foundations and usefulness of different consensus theoretic methods are discussed in conjunction with pattern recognition. Weight selections for different data sources are considered and modeling of non-Gaussian data is investigated. The application of consensus theory in pattern recognition is tested on two data sets: (1) multisource remote sensing and geographic data, and (2) very-high-dimensional remote sensing data. The results obtained using consensus theoretic methods are found to compare favorably with those obtained using well-known pattern recognition methods. The consensus theoretic methods can be applied in cases where the Gaussian maximum likelihood method cannot. Also, the consensus theoretic methods are computationally less demanding than the Gaussian maximum likelihood method and provide a means for weighting data sources differently.< > |
| doi_str_mv | 10.1109/21.156582 |
| format | Article |
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The foundations and usefulness of different consensus theoretic methods are discussed in conjunction with pattern recognition. Weight selections for different data sources are considered and modeling of non-Gaussian data is investigated. The application of consensus theory in pattern recognition is tested on two data sets: (1) multisource remote sensing and geographic data, and (2) very-high-dimensional remote sensing data. The results obtained using consensus theoretic methods are found to compare favorably with those obtained using well-known pattern recognition methods. The consensus theoretic methods can be applied in cases where the Gaussian maximum likelihood method cannot. Also, the consensus theoretic methods are computationally less demanding than the Gaussian maximum likelihood method and provide a means for weighting data sources differently.< ></description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Cybernetics</subject><subject>Data mining</subject><subject>Earth Observing System</subject><subject>Exact sciences and technology</subject><subject>Laboratories</subject><subject>Maximum likelihood estimation</subject><subject>Pattern recognition</subject><subject>Pattern recognition. Digital image processing. 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| ispartof | IEEE transactions on systems, man, and cybernetics, 1992-07, Vol.22 (4), p.688-704 |
| issn | 0018-9472 2168-2909 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Cybernetics Data mining Earth Observing System Exact sciences and technology Laboratories Maximum likelihood estimation Pattern recognition Pattern recognition. Digital image processing. Computational geometry Radar remote sensing Remote sensing Soil Statistics Testing |
| title | Consensus theoretic classification methods |
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