Patricia Melin,Emanuel Ontiveros-Robles,Oscar Castillo
New Medical Diagnosis Models Based on Generalized Type-2 Fuzzy Logic
New Medical Diagnosis Models Based on Generalized Type-2 Fuzzy Logic
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This book presents different experimental results as evidence of the good results obtained compared with respect to conventional approaches and literature references based on fuzzy logic. It focuses on the automatic design of classifiers and explores alternatives to uncertainty modeling, rules-selection, and optimization. The main objective is to generate competitive fuzzy diagnosis systems.
Format: Paperback / softback
Length: 78 pages
Publication date: 04 June 2021
Publisher: Springer Nature Switzerland AG
The evolution of intelligence systems for decision-making has achieved remarkable success, with these systems becoming increasingly intelligent and invaluable to decision-makers. One of the critical domains in decision-making is medical diagnosis, where various intelligence systems offer expert assistance in performing diagnoses. Some of these methods include artificial neural networks, which possess remarkable power in identifying trends, support vector machines, which mitigate overfitting issues, and statistical approaches, such as Bayesian inference. However, the present research focuses on one of the most significant types of intelligent systems: fuzzy systems. The primary objective of this work is to generate fuzzy diagnosis systems that offer competitive classifiers for use in diagnosis systems. To achieve this, we have proposed a methodology for the automatic design of classifiers, with a particular emphasis on Generalized Type-2 Fuzzy Logic. The uncertainty handling provided by fuzzy logic is crucial for achieving competitiveness with other methods. Additionally, we have explored various alternatives for uncertainty modeling, rule selection, and optimization. Furthermore, we present extensive experimental results that demonstrate the excellent outcomes obtained when compared to conventional approaches and literature references based on Fuzzy Logic.
Weight: 454g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030750961
Edition number: 1st ed. 2021
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