Linked Life Data

19342348

Source:http://linkedlifedata.com/resource/pubmed/id/19342348

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2009-4-30
pubmed:abstractText
In this paper, the multiclass supervised training problem is considered when a discrete set of classes is assumed. Upon generating affine models for finite data sets, we have observed the invariance of certain measures of performance after a trained classifier has been presented with test data of unknown classification. Specifically, after constructing mappings between training vectors and their desired targets, the class membership and ranking of test data has been found to remain either invariant or close to invariant under a transformation of the set of target vectors. Therefore, we derive conditions explaining how this type of invariance can arise when the multiclass problem is phrased in the context of linear networks. A bioinformatics example is then presented in order to demonstrate various principles outlined in this work.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1941-0093
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
745-57
pubmed:dateRevised
2009-10-28
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
The effect of target vector selection on the invariance of classifier performance measures.
pubmed:affiliation
Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA. eric.sakk@morgan.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, N.I.H., Extramural