Fuzzy rule interpolation (FRI) facilitates approximate reasoning in fuzzy rule-based systems only with sparse knowledge available, remedying the limitation of conventional compositional rule of inference working with a dense rule base. Most of the existing FRI work assumes equal significance of the conditional attributes in the rules while performing interpolation. Recently, interesting techniques have been reported for achieving weighted interpolative reasoning. However, they are either particularly tailored to perform classification problems only or employ attribute weights that are obtained using additional information (rather than just the given rules), without integrating them with the associated FRI procedure. This paper presents a weighted rule interpolation scheme for performing prediction tasks by the use of fuzzy sparse knowledge only. The weights of rule conditional attributes are learned from a given rule base to discriminate the relative significance of each individual attribute and are integrated throughout the internal mechanism of the FRI process. This scheme is demonstrated using the popular scale and move transformation-based FRI for resolving prediction problems, systematically evaluated on 12 benchmark prediction tasks. The performance is compared with the relevant state-of-the-art FRI techniques, showing the efficacy of the proposed approach.

中文翻译：

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通过加权规则插值进行基于知识的模糊预测


模糊规则插值（FRI）仅在可用稀疏知识的情况下促进基于模糊规则的系统中的近似推理，弥补了使用密集规则库的传统推理组合规则的局限性。大多数现有的 FRI 工作在执行插值时都假设规则中的条件属性具有同等重要性。最近，报道了用于实现加权插值推理的有趣技术。然而，它们要么专门定制用于执行分类问题，要么采用使用附加信息（而不仅仅是给定规则）获得的属性权重，而不将它们与相关的 FRI 过程集成。本文提出了一种仅使用模糊稀疏知识来执行预测任务的加权规则插值方案。规则条件属性的权重是从给定的规则库中学习的，以区分每个单独属性的相对重要性，并集成到 FRI 过程的整个内部机制中。该方案使用流行的基于尺度和移动变换的 FRI 来解决预测问题，并在 12 个基准预测任务上进行系统评估。将性能与相关最先进的 FRI 技术进行比较，显示了所提出方法的有效性。