Feature Selection Based on Fuzzy Mutual Information

机译：基于模糊互信息的特征选择

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摘要

In the framework of fuzzy rule-based models for regression problems, we propose a novel approach to feature selection based on the minimal-redundancy-maximal-relevance criterion. The relevance of a feature is measured in terms of a novel definition of fuzzy mutual information between the feature and the output variable. The redundancy is computed as the average fuzzy mutual information between the feature and the just selected features. The approach results to be particularly suitable for selecting features before designing fuzzy rule-based systems (FRBSs). We tested our approach on twelve regression problems using Mamdani FRBSs built by applying the Wang and Mendel algorithm. We show that our approach is particularly effective in selecting features by comparing the mean square errors achieved by the Mamdani FRBSs generated using the features selected by a state of the art feature selection algorithm and by our approach.

机译：在基于模糊规则的回归问题模型框架下，我们提出了一种基于最小冗余最大相关性准则的特征选择新方法。根据对特征和输出变量之间的模糊互信息的新颖定义来度量特征的相关性。冗余计算为特征和刚选择的特征之间的平均模糊互信息。该方法的结果特别适合在设计基于模糊规则的系统（FRBS）之前选择特征。我们使用通过应用Wang和Mendel算法构建的Mamdani FRBS对十二种回归问题进行了测试。我们通过比较由先进特征选择算法和我们的方法选择的特征生成的Mamdani FRBS所实现的均方误差，表明我们的方法在选择特征方面特别有效。

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来源
《Fuzzy logic and applications》|2013年|36-43|共8页
会议地点 Genoa(IT)
作者
Michela Antonelli; Pietro Ducange; Francesco Marcelloni;
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作者单位

Dipartimento di Ingegneria dell'Informazione, University of Pisa Pisa, Italy;

Dipartimento di Ingegneria dell'Informazione, University of Pisa Pisa, Italy;

Dipartimento di Ingegneria dell'Informazione, University of Pisa Pisa, Italy;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Feature Selection; Fuzzy Mutual Information; Regression Problems; High Dimensional Datasets;

机译：特征选择；模糊互信息；回归问题；高维数据集;

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Feature Selection Based on Fuzzy Mutual Information

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