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.
Original language | English |
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Title of host publication | Advances in Fuzzy Logic and Technology 2017 - Proceedings of |
Subtitle of host publication | EUSFLAT-2017 – The 10th Conference of the European Society for Fuzzy Logic and Technology, IWIFSGN’2017 – The 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets |
Publisher | Springer |
Pages | 477-486 |
Number of pages | 10 |
Volume | 641 |
ISBN (Print) | 9783319668291 |
DOIs | |
Publication status | Published - 2018 |
Event | 10th Conference of the European Society for Fuzzy Logic and Technology, (EUSFLAT 2017) and 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN 2017, 11-15 September 2017, Warsaw, Poland - Warsaw, Poland Duration: 11 Sept 2017 → 15 Sept 2017 http://www.eusflat2017.ibspan.waw.pl/ |
Publication series
Name | Advances in Intelligent Systems and Computing |
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Volume | 641 |
ISSN (Print) | 2194-5357 |
Conference
Conference | 10th Conference of the European Society for Fuzzy Logic and Technology, (EUSFLAT 2017) and 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN 2017, 11-15 September 2017, Warsaw, Poland |
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Abbreviated title | EUSFLAT2017 |
Country/Territory | Poland |
City | Warsaw |
Period | 11/09/17 → 15/09/17 |
Internet address |
Keywords
- Feature selection
- Fuzzy models
- Mutual information
- Number of features