Setzkorn Christian, Paton Ray C
Department of Computer Science, University of Liverpool, Prescot Street, Liverpool L7 8XP, UK.
Biosystems. 2005 Aug;81(2):101-12. doi: 10.1016/j.biosystems.2005.02.003.
Extracting comprehensible and general classifiers from data in the form of rule systems is an important task in many problem domains. This study investigates the utility of a multi-objective evolutionary algorithm (MOEA) for this task. Multi-objective evolutionary algorithms are capable of finding several trade-off solutions between different objectives in a single run. In the context of the present study, the objectives to be optimised are the complexity of the rule systems, and their fit to the data. Complex rule systems are required to fit the data well. However, overly complex rule systems often generalise poorly on new data. In addition they tend to be incomprehensible. It is, therefore, important to obtain trade-off solutions that achieve the best possible fit to the data with the lowest possible complexity. The rule systems produced by the proposed multi-objective evolutionary algorithm are compared with those produced by several other existing approaches for a number of benchmark datasets. It is shown that the algorithm produces less complex classifiers that perform well on unseen data.
从规则系统形式的数据中提取可理解的通用分类器是许多问题领域中的一项重要任务。本研究调查了多目标进化算法（MOEA）在此任务中的效用。多目标进化算法能够在单次运行中找到不同目标之间的多个权衡解决方案。在本研究的背景下，要优化的目标是规则系统的复杂性及其对数据的拟合度。复杂的规则系统需要很好地拟合数据。然而，过于复杂的规则系统在新数据上的泛化能力往往较差。此外，它们往往难以理解。因此，获得能够以尽可能低的复杂性实现对数据的最佳拟合的权衡解决方案非常重要。针对多个基准数据集，将所提出的多目标进化算法产生的规则系统与其他几种现有方法产生的规则系统进行了比较。结果表明，该算法产生的分类器复杂性较低，在未见数据上表现良好。
