A Novel Method for Building Regression Tree Models for QSAR Based on Artificial Ant Colony Systems

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Abstract
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.