- author = "Ray L. Somorjai and Alexander E. Nikulin and Nic Pizzi and Dick Jackson and Gordon Scarth and Brion Dolenko and Heather Gordon and Peter Russell and Cynthia L. Lean and Leigh Delbridge and Carolyn E. Mountford and Ian C. P. Smith",
- title = "Computerized Consensus Diagnosis: A Classification Strategy for the Robust Analysis of {MR} spectra. {I}. Application to {1H} Spectra of Thyroid Neoplasms",
- journal = "Magnetic Resonance Medicine",
- year = "1995",
- volume = "33",
- number = "2",
- pages = "257--263",
- month = feb,
- keywords = "genetic algorithms, genetic programming, computerised consensus diagnosis, robust classification, thyroid neoplasms, proton magnetic resonance spectrum, LDA, ANN, GEPPETTO",
- ISSN = "0740-3194",
-
URL = "
http://www.interscience.wiley.com/jpages/0740-3194/",
-
URL = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=7707918&dopt=Abstract",
-
DOI = "
doi:10.1002/mrm.1910330217",
- size = "7 pages",
- abstract = "We introduce and apply a new classification strategy we call computerised consensus diagnosis (CCD). Its purpose is to provide robust, reliable classification of biomedical data. The strategy involves the cross-validated training of several classifiers of diverse conceptual and methodological origin on the same data, and appropriately combining their outcomes. The strategy is tested on proton magnetic resonance spectra of human thyroid biopsies, which are successfully allocated to normal or carcinoma classes. We used Linear Discriminant Analysis, a Neural Net-based method, and Genetic Programming as independent classifiers on two spectral regions, and chose the median of the six classification outcomes as the consensus. This procedure yielded 100percent specificity and 100percent sensitivity on the training sets, and 100percent specificity and 98percent sensitivity on samples of known malignancy in the test sets. We discuss the necessary steps any classification approach must take to guarantee reliability, and stress the importance of fuzziness and undecidability in robust classification.",
-
notes = "PMID: 7707918 [PubMed - indexed for MEDLINE]
oai:espace.library.uq.edu.au:UQ:614804
consensus means taking 2 of 3 vote from the three different classifiers (GP, LDA and NN). PCA",
