J Biomed Inform - Class proximity measures--dissimilarity-based classification and display of high-dimensional data.

Tópicos

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{ age(1611) year(1155) adult(843) }
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{ gene(2352) biolog(1181) express(1162) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

For two-class problems, we introduce and construct mappings of high-dimensional instances into dissimilarity (distance)-based Class-Proximity Planes. The Class Proximity Projections are extensions of our earlier relative distance plane mapping, and thus provide a more general and unified approach to the simultaneous classification and visualization of many-feature datasets. The mappings display all L-dimensional instances in two-dimensional coordinate systems, whose two axes represent the two distances of the instances to various pre-defined proximity measures of the two classes. The Class Proximity mappings provide a variety of different perspectives of the dataset to be classified and visualized. We report and compare the classification and visualization results obtained with various Class Proximity Projections and their combinations on four datasets from the UCI data base, as well as on a particular high-dimensional biomedical dataset.

Resumo Limpo

twoclass problem introduc construct map highdimension instanc dissimilar distancebas classproxim plane class proxim project extens earlier relat distanc plane map thus provid general unifi approach simultan classif visual manyfeatur dataset map display ldimension instanc twodimension coordin system whose two axe repres two distanc instanc various predefin proxim measur two class class proxim map provid varieti differ perspect dataset classifi visual report compar classif visual result obtain various class proxim project combin four dataset uci data base well particular highdimension biomed dataset

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