An Input-weighted, Multi-Objective Evolutionary Fuzzy Classifier, for Alcohol Classification
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Budapest Tech
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The success of the evolutionary computational methods in scanning at problem's solution space and the ability to produce robust solutions, are important advantages for fuzzy systems, especially in terms of interpretability and accuracy . Many techniques have been introduced for multi-objective evolutionary fuzzy classifiers by considering this advantage. However, these techniques are mostly fuzzy rule-based methods. In this study, instead of designing an optimal rule table or determining optimal rule weights, the inputs are weighted, and no rules are used. The average of the degrees of membership obtained with their Membership Function (MF) is calculated as the input membership degree (mu Inp) for each input. The mu Inps are then weighted, and a single coefficient is generated to be used for the output. With the output, results are obtained for different objective functions. The weights of the inputs and the MFs parameters of all variables (inputs and outputs) are optimized with NSGA-II. The performance of the method has been tested for alcohol classification. As a result, it has been proven that the method can generate designs that can classify at shallow error levels with different sensors at different gas concentrations. In addition, it has been observed that the proposed method produces more successful solutions for alcohol classification problems when compared to other MOEFC techniques.
Açıklama
Anahtar Kelimeler
Multi-Objective Fuzzy Classifier, Multi-Objective Optimization, Input-Weighted Multi-Objective Fuzzy Classifier
Kaynak
Acta Polytechnica Hungarica
WoS Q Değeri
Q3
Scopus Q Değeri
Cilt
19
Sayı
10
