A novel method for building regression tree models for QSAR based on artificial ant colony systems.
Among the multitude of learning algorithms that can be employed for deriving quantitative structure-activity relationships, regression trees have the advantage of being able to handle large data sets, dynamically perform the key feature selection, and yield readily interpretable models. A conventional method of building a regression tree model is recursive partitioning, a fast greedy algorithm that works well in many, but not all, cases. This work introduces a novel method of data partitioning based on artificial ants. This method is shown to perform better than recursive partitioning on three well-studied data sets.
Izrailev
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S
3-Dimensional Pharmaceuticals, Inc., Exton, Pennsylvania 19341, USA. sergei@3dp.com
Agrafiotis
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D
D000465:Algorithms; D000818:Animals; D001000:Ants; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship