Abstract This work presents FuzzyFeatureRank, a new feature reduction method inspired on PageRank to reduce the dimensionality of the feature space in supervised classification problems. More precisely, as it relies on a weighted directed graph, it is ultimately inspired on TextRank, a PageRank based method that adds weights to the edges to express the strength of the connections between nodes. The method is based on dividing each original feature used to describe the data into a set of fuzzy predicates and then ranking all of them by their ability to differentiate among classes in the light of the training set. In order to do that, both the information gained by each predicate and their redundancy with other already selected predicates are taken into account. The fuzzy predicates with the best scores can then be used as a reduced input to construct fuzzy classifiers that consider only the preselected predicates to build the antecedents of the fuzzy rules. The novelty of the proposal relies on being an approach halfway between feature selection and feature extraction approaches, being able to improve the discrimination ability of the original features but preserving the interpretability of the new features in the sense that they are fuzzy expressions. The experimental results support the suitability of the proposal.

中文翻译：

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模糊特征等级。通过模糊表达式将顺序带入模糊分类器

摘要 这项工作提出了 FuzzyFeatureRank，这是一种受 PageRank 启发的新特征减少方法，用于减少监督分类问题中特征空间的维数。更准确地说，由于它依赖于加权有向图，因此最终受到了 TextRank 的启发，TextRank 是一种基于 PageRank 的方法，该方法向边添加权重以表达节点之间的连接强度。该方法基于将用于描述数据的每个原始特征划分为一组模糊谓词，然后根据训练集区分类别的能力对所有这些谓词进行排序。为了做到这一点，每个谓词获得的信息及其与其他已经选择的谓词的冗余都被考虑在内。然后可以将具有最佳分数的模糊谓词用作简化输入以构建仅考虑预选谓词来构建模糊规则的前提的模糊分类器。该提案的新颖之处在于它介于特征选择和特征提取方法之间，能够提高原始特征的判别能力，同时保留新特征的可解释性，因为它们是模糊表达。实验结果支持该提议的适用性。能够提高原始特征的辨别能力，但保留新特征的可解释性，因为它们是模糊表达。实验结果支持该提议的适用性。能够提高原始特征的辨别能力，但保留新特征的可解释性，因为它们是模糊表达。实验结果支持该提议的适用性。