Abstract
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.
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This work is partially supported by Philips Research within the scope of the BrainBridge Program.
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Chen, P., Wilbik, A., van Loon, S., Boer, AK., Kaymak, U. (2018). Finding the Optimal Number of Features Based on Mutual Information. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-66830-7_43
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DOI: https://doi.org/10.1007/978-3-319-66830-7_43
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