Efficient inference models for classification problems with a high number of fuzzy rules

2021 ◽  
pp. 108164
Author(s):  
Leonardo Jara ◽  
Rubén Ariza-Valderrama ◽  
Juan Fernández-Olivares ◽  
Antonio González ◽  
Raúl Pérez
Keyword(s):  
Fuzzy Rules ◽  
Classification Problems ◽  
Inference Models

GENERATING FUZZY RULES FROM TRAINING DATA CONTAINING NOISE FOR HANDLING CLASSIFICATION PROBLEMS

2002 ◽  
Vol 33 (7) ◽  
pp. 723-748 ◽  
Author(s):  
SHYI-MING CHEN ◽  
CHENG-HSUAN KAO ◽  
CHENG-HAO YU
Keyword(s):  
Fuzzy Rules ◽  
Training Data ◽  
Classification Problems

Optimization of Constrained SIRMs Connected Type Fuzzy Inference Model Using Two-Phase Simplex Method

2018 ◽  
Vol 22 (2) ◽  
pp. 172-175
Author(s):  
Takeshi Nagata ◽  
Hirosato Seki ◽  
Hiroaki Ishii ◽  
◽  
◽  
...  
Keyword(s):  
Medical Diagnosis ◽  
Simplex Method ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Inference Model ◽  
Two Phase ◽  
Inference Models ◽  
Learning Rules ◽  
Proposed Model ◽  
Single Input

Single Input Rule Modules connected fuzzy inference model (SIRMs model, for short) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference models. However, it is difficult to understand the meaning of the weight for the SIRMs model because the value of the weight has no restriction in the learning rules. Therefore, the paper proposes a constrained SIRMs model in which the weights are in [0,1] by using two-phase simplex method. Moreover, it shows that the applicability of the proposed model by applying it to a medical diagnosis.

COMBINING ADABOOST WITH PREPROCESSING ALGORITHMS FOR EXTRACTING FUZZY RULES FROM LOW QUALITY DATA IN POSSIBLY IMBALANCED PROBLEMS

2012 ◽  
Vol 20 (supp02) ◽  
pp. 51-71 ◽  
Author(s):  
ANA PALACIOS ◽  
LUCIANO SANCHEZ ◽  
INES COUSO
Keyword(s):  
Real World ◽  
Binary Classification ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Quality Data ◽  
Classification Problems ◽  
Rule Based ◽  
Adaboost Algorithm ◽  
Wide Range ◽  
Real World Problems

An extension of the Adaboost algorithm for obtaining fuzzy rule-based systems from low quality data is combined with preprocessing algorithms for equalizing imbalanced datasets. With the help of synthetic and real-world problems, it is shown that the performance of the Adaboost algorithm is degraded in presence of a moderate uncertainty in either the input or the output values. It is also established that a preprocessing stage improves the accuracy of the classifier in a wide range of binary classification problems, including those whose imbalance ratio is uncertain.

GENERATING FUZZY RULES BY GENETIC METHOD AND ITS APPLICATIONS

1998 ◽  
Vol 07 (04) ◽  
pp. 399-413 ◽  
Author(s):  
MAL-REY LEE
Keyword(s):  
Optimization Problem ◽  
Fitness Function ◽  
Descent Method ◽  
Combinatorial Optimization Problem ◽  
Fuzzy Rules ◽  
Membership Functions ◽  
Classification Problems ◽  
Real Numbers ◽  
Genetic Method ◽  
Simulation Results

This paper proposes a GA method for choosing an appropriate set of fuzzy rules for classification problems. The aim of the proposed method is to find a minimum set of fuzzy rules that can correctly classify all training patterns. The number of inference rules and the shapes of the membership functions in the antecedent part of the fuzzy rules are determined by the genetic algorithms. The real numbers in the consequent parts of the fuzzy rules are obtained through the use of the descent method. A fitness function is used to maximize the number of correctly classified patterns, and to minimize the number of fuzzy rules. A solution obtained by the genetic algorithm is a set of fuzzy rules, and its fitness is determined by the two objectives, in a combinatorial optimization problem. In order to demonstrate the effectiveness of the proposed method, computer simulation results are shown.

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