Fuzzy classification and fast rules for refusal in the QSAR problem

A new approach for analyzing the “molecule-descriptor” matrix for the QSAR problem (Quantitative Structure-Activity Relationship) based on a fuzzy cluster structure of the learning sample is presented. The ways for generating fast rules for refusing prediction and searching the spikes in the learnin...

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Bibliographic Details
Published inPattern recognition and image analysis Vol. 21; no. 3; pp. 542 - 544
Main Authors Prokhorov, E. I., Ponomareva, L. A., 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
Clustering
Computational efficiency
Computer Science
Fuzzy
Fuzzy logic
Fuzzy set theory
Graphs
Image Processing and Computer Vision
Learning
Mapping
Mathematical models
Optimization
Pattern Recognition
Studies
Feature Vector
Fuzzy Partition
Prediction Quality
Fuzzy Cluster
Cluster Structure
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Summary:A new approach for analyzing the “molecule-descriptor” matrix for the QSAR problem (Quantitative Structure-Activity Relationship) based on a fuzzy cluster structure of the learning sample is presented. The ways for generating fast rules for refusing prediction and searching the spikes in the learning sample are described. For this purpose, a special space of descriptors, simple for calculation, is introduced. The ways for optimizing the discriminant function according to fuzzy clustering parameters are examined. Highly predictive models based on the presented approach have been generated. The models are compared, and the efficiency of the described methods is revealed.
Bibliography:ObjectType-Article-1
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ISSN:1054-6618
1555-6212
DOI:10.1134/S105466181102091X