Using Symbiotic Evolution to Improve the Predictive Accuracy for Inductive Logic Programming
Using Symbiotic Evolution to Improve the Predictive Accuracy for Inductive Logic Programming
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DOI：
10.1527/tjsai.17.431
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发表时间：
2002-12
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通讯作者：
Noriko Otani;H. Ohwada
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文献类型：
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作者：
Noriko Otani;H. Ohwada
本文介绍了归纳逻辑规划（ILP）中最优假设搜索的一种方法。该方法基于遗传算法（GA）的一种变体共生进化，以提高对未知样本分类的预测精度。Progol是典型的ILP系统，它采用一个细化算子，找到一个包含最具体假设的最优假设。Progol侧重于对训练数据具有最大解释力的假设。然而，ILP系统应该通过其对未知数据的解释能力来评估。与此相反，本文提出的方法利用共生进化产生一个假设，它维持和进化两个种群：问题的部分解的种群和由几个部分解组合在一起形成的完整解的种群。共生进化可以对部分解和完全解进行平衡优化。我们假设遗传算法中结果的多样性增加了对未知数据的适应度。我们开发了一个ILP系统，称为ILP/SE，它在假设搜索任务中使用共生进化，在其他任务中使用Progol的学习算法。ILP/SE利用在重复执行中获得的多个假设，以多数判断未知数据的类别。利用诱变数据集进行了实验，验证了ILP/SE的性能。结果表明，ILP/SE方法优于先前使用Progol的方法
This paper describes a method for optimal hypothesis search in Inductive Logic Programming(ILP). The method is based on symbiotic evolution, a variant of genetic algorithm (GA), for improving the predictive accuracy in classifying unknown examples. Progol, the representative ILP system, employs a refinement operator and finds an optimal hypothesis which subsumes the most specific hypothesis. Progol focuses on a hypothesis which has maximum explanatory power for training data. However, ILP systems should be evaluated by their explanatory powers for unknown data. In contrast, the proposed method produces a hypothesis using symbiotic evolution, which maintains and evolves two populations: a population of partial solutions to the problem and a population of complete solutions which are formed by grouping several partial solutions together. Symbiotic evolution can conduct balanced optimization of partial solutions and complete solutions. We postulate that the diversity of the results in GA increases the fitness to unknown data. We have developed an ILP system called ILP/SE, which uses symbiotic evolution for the hypothesis search task and uses the learning algorithm of Progol for the other task. ILP/SE judges the class of unknown data by majority using multiple hypothesises obtained in repeated execution. Experiments were conducted to show the performance of ILP/SE using the mutagenesis dataset. The result indicates that the ILP/SE approach outperforms the previous method using Progol for