An intelligent hybrid classification algorithm integrating fuzzy rule-based extraction and harmony search optimization: Medical diagnosis applications

2021 ◽  
Vol 220 ◽  
pp. 106943
Author(s):  
Seyed Mohsen Mousavi ◽  
Salwani Abdullah ◽  
Seyed Taghi Akhavan Niaki ◽  
Saeed Banihashemi
Keyword(s):  
Medical Diagnosis ◽  
Harmony Search ◽  
Fuzzy Rule ◽  
Classification Algorithm ◽  
Rule Based ◽  
Search Optimization ◽  
Hybrid Classification

scholarly journals An interpretable fuzzy rule-based classification methodology for medical diagnosis

2009 ◽  
Vol 47 (1) ◽  
pp. 25-41 ◽  
Author(s):  
Ioannis Gadaras ◽  
Ludmil Mikhailov
Keyword(s):  
Medical Diagnosis ◽  
Fuzzy Rule ◽  
Rule Based

An Interpretability-Accuracy Tradeoff in Learning Parameters of Intuitionistic Fuzzy Rule-Based Systems

2016 ◽  
Vol 20 (5) ◽  
pp. 773-787
Author(s):  
Yanni Wang ◽  
◽  
Yaping Dai ◽  
Yu-Wang Chen ◽  
Witold Pedrycz ◽  
...  
Keyword(s):  
Medical Diagnosis ◽  
Fuzzy Rule ◽  
Programming Method ◽  
Parameter Learning ◽  
Data Set ◽  
Rule Based ◽  
Intuitionistic Fuzzy ◽  
Objective Programming ◽  
Accuracy Tradeoff ◽  
Rule Based Systems

Parameter learning of Intuitionistic Fuzzy Rule-Based Systems (IFRBSs) is discussed and applied to medical diagnosis with intent of establishing a sound tradeoff between interpretability and accuracy. This study aims to improve the accuracy of IFRBSs without sacrificing its interpretability. This paper proposes an Objective Programming Method with an Interpretability-Accuracy tradeoff (OPMIA) to learn the parameters of IFRBSs by tuning the types of membership and non-membership functions and by adjusting adaptive factors and rule weights. The proposed method has been validated in the context of a medical diagnosis problem and a well-known publicly available auto-mpg data set. Furthermore, the proposed method is compared to Objective Programming Method not considering the interpretability (OPMNI) and Objective Programming Method based on Similarity Measure (OPMSM). The OPMIA helps achieve a sound a tradeoff between accuracy and interpretability and demonstrates its advantages over the other two methods.

scholarly journals Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system

2014 ◽  
Vol 20 ◽  
pp. 103-111 ◽  
Author(s):  
José Antonio Sanz ◽  
Mikel Galar ◽  
Aranzazu Jurio ◽  
Antonio Brugos ◽  
Miguel Pagola ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Classification System ◽  
Medical Diagnosis ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Interval Valued

Cost-Sensitive Classification for Medical Diagnosis

2008 ◽  
pp. 297-302 ◽  
Author(s):  
Gerald Schaefer ◽  
Tomoharu Nakashima ◽  
Yasuyuki Yokota
Keyword(s):  
Pattern Classification ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Breast Cancer Dataset ◽  
Classification Problems ◽  
Rule Based ◽  
Cost Sensitive Classification ◽  
Minimisation Problem ◽  
Rule Based Classifier

In this article, we present a cost-sensitive approach to medical diagnosis based on fuzzy rule-based classification (Schaefer, Nakashima, Yokota, & Ishibuchi, 2007). While fuzzy rule-based systems have been mainly employed for control problems (Lee, 1990) more recently they have also been applied to pattern classification problems (Ishibuchi & Nakashima, 1999; Nozaki, Ishibuchi, & Tanaka, 1996). We modify a fuzzy rule-based classifier to incorporate the concept of weight which can be considered as the cost of an input pattern being misclassified. The pattern classification problem is thus reformulated as a cost minimisation problem. Based on experimental results on the Wisconsin breast cancer dataset, we demonstrate the efficacy of our approach. We also show that the application of a learning algorithm can further improve the classification performance of our classifier.

A Fuzzy Rule-Based Model of Human Problem Solving

1982 ◽  
Author(s):  
Ruston M. Hunt ◽  
William B. Rouse
Keyword(s):  
Problem Solving ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Human Problem
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