A way to increase the prediction quality for the large set of molecular graphs by using the k-NN classifier

A way to solve the QSAR problem (Quantitative Structure-Activity Relationship) by selecting the molecular graph descriptors using the k -NN classifier is presented. The predictive models by using the search and without it are generated and compared, and the results of comparison are presented. The s...

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Bibliographic Details
Published inPattern recognition and image analysis Vol. 21; no. 3; pp. 545 - 548
Main Authors Perevoznikov, A. V., Shestov, A. M., Permyakov, E. A., Kumskov, M. I.
Format Journal Article
LanguageEnglish
Published Dordrecht SP MAIK Nauka/Interperiodica 01.09.2011
Springer Nature B.V
Subjects
Algorithms
Applied Problems
Classification
Classifiers
Comparative analysis
Computer Science
Euclidean space
Graphs
Image analysis
Image Processing and Computer Vision
Mathematical models
Pattern Recognition
Searching
Stability
Studies
Learning Sample
Comparative Molecular Field Analysis
Descriptor Matrix
Molecular Graph
Quantitative Structure Property Relationship
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Summary:A way to solve the QSAR problem (Quantitative Structure-Activity Relationship) by selecting the molecular graph descriptors using the k -NN classifier is presented. The predictive models by using the search and without it are generated and compared, and the results of comparison are presented. The stability of the discriminant function quality of generation is tested by using the test sample.
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SourceType-Scholarly Journals-1
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ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661811020866