Linked Life Data

16935929

Source:http://linkedlifedata.com/resource/pubmed/id/16935929

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
21
pubmed:dateCreated
2006-10-25
pubmed:abstractText
MOTIVATION: Machine learning methods such as neural networks, support vector machines, and other classification and regression methods rely on iterative optimization of the model quality in the space of the parameters of the method. Model quality measures (accuracies, correlations, etc.) are frequently overly optimistic because the training sets are dominated by particular families and subfamilies. To overcome the bias, the dataset is usually reduced by filtering out closely related objects. However, such filtering uses fixed similarity thresholds and ignores a part of the training information. RESULTS: We suggested a novel approach to calculate prediction model quality based on assigning to each data point inverse density weights derived from the postulated distance metric. We demonstrated that our new weighted measures estimate the model generalization better and are consistent with the machine learning theory. The Vapnik-Chervonenkis theorem was reformulated and applied to derive the space-uniform error estimates. Two examples were used to illustrate the advantages of the inverse density weighting. First, we demonstrated on a set with a built-in bias that the unweighted cross-validation procedure leads to an overly optimistic quality estimate, while the density-weighted quality estimates are more realistic. Second, an analytical equation for weighted quality estimates was used to derive an SVM model for signal peptide prediction using a full set of known signal peptides, instead of the usual filtered subset.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2597-603
pubmed:dateRevised
2009-11-4
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Weighted quality estimates in machine learning.
pubmed:affiliation
Molsoft LLC, 3366 North Torrey Pines Court Suite 300, San Diego, CA 92037, USA. levon@molsoft.com
pubmed:publicationType
Journal Article, Evaluation Studies