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[48720] Artykuł:

An improved multi-objective evolutionary optimization of data-mining-based fuzzy decision support systems

Czasopismo: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)   Strony: 2227-2234
ISSN:  1544-5615
ISBN:  978-1-5090-0625-0
Wydawca:  IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Opublikowano: 2016
Seria wydawnicza:  IEEE International Fuzzy Systems Conference Proceedings
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Procent
udziału		 Liczba
punktów
Marian Bolesław Gorzałczany orcid logoWEAiIKatedra Informatyki, Elektroniki i Elektrotechniki *507.50  
Filip Rudziński orcid logoWEAiIKatedra Informatyki, Elektroniki i Elektrotechniki *507.50  

Grupa MNiSW:  Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science)
Punkty MNiSW: 15
Klasyfikacja Web of Science: Proceedings Paper

Pełny tekstPełny tekst     DOI LogoDOI     Web of Science Logo Web of Science    

Abstract:

The paper presents an approach to designing from data fuzzy decision systems (fuzzy rule-based classifiers (FRBCs)) by means of four multi-objective evolutionary optimization algorithms (MOEOAs) including the well-known NSGA-II, ϵ-NSGA-II, SPEA2, and our generalization of SPEA2 (referred to as SPEA3). The advantages of SPEA3 (a better-balanced distribution and a higher spread of solutions than for SPEA2) are shown using selected benchmark tests. The main building blocks of our FRBC and the main components of its MOEOA-based optimization are briefly presented. The proposed FRBCs with genetically optimized accuracy-interpretability trade-off are effective and modern tools for intelligent decision support in various areas of applications. In this paper, the application to designing credit-granting decision support system based on Statlog (German Credit Approval) financial benchmark data set is presented. A comparison of our approach employing various MOEOAs is also carried out.


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