期刊
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
卷 41, 期 1, 页码 176-180出版社
AMER CHEMICAL SOC
DOI: 10.1021/ci000336s
关键词
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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.
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