A novel method for building regression tree models for QSAR based on artificial ant colony systems
A novel method for building regression tree models for QSAR based on artificial ant colony systems
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DOI：
10.1021/ci000036s
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发表时间：
2001-01-01
期刊：
影响因子：
--
通讯作者：
Agrafiotis, D
中科院分区：
文献类型：
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作者：
Izrailev, S;Agrafiotis, D
在可用于推导定量结构-活性关系的众多学习算法中，回归树具有能够处理大数据集、动态执行关键特征选择以及产生易于解释的模型的优势。构建回归树模型的传统方法是递归分区，这是一种快速的贪婪算法，在许多情况下(但不是所有情况)都能很好地工作。提出了一种新的基于人工蚂蚁的数据划分方法。该方法在三个经过充分研究的数据集上表现出比递归划分更好的性能。
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.