Clustering Based on Fuzzy Rule-Based Classifier

2014 ◽  
pp. 233-242
Author(s):  
D. K. Behera ◽  
P. K. Patra
Keyword(s):  
Fuzzy Rule ◽  
Rule Based ◽  
Rule Based Classifier

A Genetic Fuzzy-Rule-Based Classifier for Land Cover Classification From Hyperspectral Imagery

2012 ◽  
Vol 50 (1) ◽  
pp. 130-148 ◽  
Author(s):  
Dimitris G. Stavrakoudis ◽  
Georgia N. Galidaki ◽  
Ioannis Z. Gitas ◽  
John B. Theocharis
Keyword(s):  
Land Cover ◽  
Hyperspectral Imagery ◽  
Fuzzy Rule ◽  
Land Cover Classification ◽  
Rule Based ◽  
Rule Based Classifier

Network anomaly detection using a fuzzy rule-based classifier

2017 ◽  
pp. 61-65
Author(s):  
Soumadip Ghosh ◽  
Arindrajit Pal ◽  
Amitava Nag ◽  
Shayak Sadhu ◽  
Ramsekher Pati
Keyword(s):  
Anomaly Detection ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Rule Based Classifier ◽  
Network Anomaly Detection

Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS

2016 ◽  
Vol 83 (1) ◽  
pp. 97-127 ◽  
Author(s):  
Binh Thai Pham ◽  
Dieu Tien Bui ◽  
Indra Prakash ◽  
M. B. Dholakia
Keyword(s):  
Fuzzy Rule ◽  
Spatial Prediction ◽  
Classifier Ensemble ◽  
Rotation Forest ◽  
Rule Based ◽  
Rule Based Classifier

scholarly journals A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 609 ◽  
Author(s):  
Marina Bardamova ◽  
Anton Konev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Rule Based ◽  
Gravitational Search ◽  
Rule Based Classifier

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

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