A Self-Tuning Fuzzy Rule-Based Classifier for Data Streams

2014 ◽  
Vol 22 (02) ◽  
pp. 293-303 ◽  
Author(s):  
Homeira Shahparast ◽  
Sam Hamzeloo ◽  
Mansoor Zolghadri Jahromi
Keyword(s):  
Data Streams ◽  
High Performance ◽  
Fuzzy Rule ◽  
Classification Problems ◽  
Online Data ◽  
Rule Based ◽  
Self Tuning ◽  
Rule Based Classifier ◽  
Concept Change ◽  
Expensive Process

In recent years, tremendous amounts of data streams are generated in different application areas. The new challenges in these data need fast and online data processing, especially in classification problems. One of the most challenging problems in field of data streams that reduces the performance of traditional methods is concept change. To handle this problem, it is necessary to update the classifier system after every alteration of the concept of data. However, updating a classifier can often be a time consuming and expensive process. In this paper, an efficient method is proposed for quickly and easily updating of a fuzzy rule-based classifier by setting a weight for each rule. Then, two online procedures for online adjustment of the rule weights are proposed. The experimental results show the high performance of these methods against a non-weighted approach.

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 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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