For high dimensional data analytics, feature selection is an indispensable preprocessing step to reduce dimensionality and keep the simplicity and interpretability of models. This is particularly important for fuzzy modeling since fuzzy models are widely recognized for their transparency and interpretability. Despite the substantial work on feature selection, there is little research on determining the optimal number of features for a task. In this paper, we propose a method to help find the optimal number of feature effectively based on mutual information.
|Publication status||Published - 2017|
|Event||Proceedings of the Conference of the European Society for Fuzzy Logic and Technology, International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets - Warsaw, Poland|
Duration: 11 Sep 2017 → 15 Sep 2017
|Conference||Proceedings of the Conference of the European Society for Fuzzy Logic and Technology, International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets|
|Abbreviated title||EUSFLAT 2017, IWIFSGN 2017|
|Period||11/09/17 → 15/09/17|