6533b82cfe1ef96bd128f42f
RESEARCH PRODUCT
Learning Improved Feature Rankings through Decremental Input Pruning for Support Vector Based Drug Activity Prediction
Francesc J. FerriVicente CerverónWladimiro Diaz-villanuevasubject
Computer scienceActive learning (machine learning)business.industryFeature vectorPattern recognitionMachine learningcomputer.software_genreKernel methodComputational learning theoryRanking SVMFeature (machine learning)Artificial intelligencePruning (decision trees)businessFeature learningcomputerdescription
The use of certain machine learning and pattern recognition tools for automated pharmacological drug design has been recently introduced. Different families of learning algorithms and Support Vector Machines in particular have been applied to the task of associating observed chemical properties and pharmacological activities to certain kinds of representations of the candidate compounds. The purpose of this work, is to select an appropriate feature ordering from a large set of molecular descriptors usually used in the domain of Drug Activity Characterization. To this end, a new input pruning method is introduced and assessed with respect to commonly used feature ranking algorithms.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2010-01-01 |