Feature Selection Based on Fuzzy Mutual Information
- 1.4k Downloads
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.
KeywordsFeature Selection Fuzzy Mutual Information Regression Problems High Dimensional Datasets
Unable to display preview. Download preview PDF.
- 5.Gerontidis, I., Petasakis, I.E.: Lumpability of absorbing markov chains and replacement chains on fuzzy partitions. In: FUZZ-IEEE, pp. 1–8 (2010)Google Scholar
- 6.Hall, M.A., Smith, L.A.: Practical Feature Subset Selection for Machine Learning (1998)Google Scholar
- 13.Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC (2007)Google Scholar