Using fuzzy rule interpolation (FRI) interpolative reasoning can be effectively performed with a sparse rule base where a given system observation does not match any fuzzy rules. While offering a potentially powerful inference mechanism, in the current literature, typical representation of fuzzy rules in FRI assumes that all attributes in the rules are of equal significance in deriving the consequents. This is a strong assumption in practical applications, thereby, often leading to less accurate interpolated results. To address this challenging problem, this paper employs feature selection (FS) techniques to adjudge the relative significance of individual attributes and therefore, to differentiate the contributions of the rule antecedents and their impact upon FRI. This is feasible because FS provides a readily adaptable mechanism for evaluating and ranking attributes, being capable of selecting more informative features. Without requiring any acquisition of real observations, based on the originally given sparse rule base, the individual scores are computed using a set of training samples that are artificially created from the rule base through an innovative reverse engineering procedure. The attribute scores are integrated within the popular scale and move transformation-based FRI algorithm (while other FRI approaches may be similarly extended following the same idea), forming a novel method for attribute ranking-supported fuzzy interpolative reasoning. The efficacy and robustness of the proposed approach is verified through systematic experimental examinations in comparison with the original FRI technique over a range of benchmark classification problems while utilizing different FS methods. A specific and important outcome that is supported by attribute ranking, only two (i.e., the least number of) nearest adjacent rules are required to perform accurate interpolative reasoning, avoiding the need of searching for and computing with multiple rules beyond the immediate neighborhood of a given observation.

中文翻译：

---

属性排序支持的基于模糊规则的插值推理


使用模糊规则插值（FRI），可以通过稀疏规则库有效地执行插值推理，其中给定的系统观察与任何模糊规则都不匹配。在提供潜在强大的推理机制的同时，在当前的文献中，FRI 中模糊规则的典型表示假设规则中的所有属性在推导结果时具有同等的重要性。这是实际应用中的一个强有力的假设，因此，通常会导致插值结果不太准确。为了解决这个具有挑战性的问题，本文采用特征选择（FS）技术来判断各个属性的相对重要性，从而区分规则前提的贡献及其对 FRI 的影响。这是可行的，因为 FS 提供了一种易于适应的机制来评估和排名属性，能够选择更多信息特征。无需获取任何真实观察结果，基于最初给定的稀疏规则库，使用一组训练样本计算各个分数，这些训练样本是通过创新的逆向工程程序从规则库人工创建的。属性分数被集成到流行的基于尺度和移动变换的 FRI 算法中（而其他 FRI 方法可以按照相同的想法进行类似的扩展），形成一种用于属性排序支持的模糊插值推理的新方法。通过与原始 FRI 技术在一系列基准分类问题上使用不同 FS 方法的系统实验检查，验证了所提出方法的有效性和鲁棒性。 属性排序支持的一个具体而重要的结果，只需要两个（即最少数量的）最近邻规则即可进行准确的插值推理，避免了对超出邻域的多个规则进行搜索和计算。给出的观察。