scholarly journals A fuzzy association rule-based classifier for imbalanced classification problems

2021 ◽  
Author(s):  
J. Sanz ◽  
M. Sesma-Sara ◽  
H. Bustince
Keyword(s):  
Association Rule ◽  
Classification Problems ◽  
Rule Based ◽  
Imbalanced Classification ◽  
Fuzzy Association Rule ◽  
Rule Based Classifier

Compact fuzzy association rule-based classifier

2008 ◽  
Vol 34 (4) ◽  
pp. 2406-2416 ◽  
Author(s):  
F PACH ◽  
A GYENESEI ◽  
J ABONYI
Keyword(s):  
Association Rule ◽  
Rule Based ◽  
Fuzzy Association Rule ◽  
Rule Based Classifier

FUSING FUZZY ASSOCIATION RULE-BASED CLASSIFIERS USING SUGENO INTEGRAL WITH ORDERED WEIGHTED AVERAGING OPERATORS

2007 ◽  
Vol 15 (06) ◽  
pp. 717-735 ◽  
Author(s):  
YI-CHUNG HU ◽  
JUNG-FA TSAI
Keyword(s):  
Association Rule ◽  
Fuzzy Classification ◽  
Weighted Averaging ◽  
Averaging Operators ◽  
Rule Based ◽  
Sugeno Integral ◽  
Ordered Weighted Averaging ◽  
Multiple Classifiers ◽  
Fuzzy Association Rule ◽  
Ordered Weighted Averaging Operators

The time or space complexity may considerably increase for a single classifier if all features are taken into account. Thus, it is reasonable to train a single classifier by partial features. Then, a set of multiple classifiers can be generated, and an aggregation of outputs from different classifiers is subsequently performed. The aim of this paper is to propose a classification system with a heuristic fusion scheme in which multiple fuzzy association rule-based classifiers with partial features are combined, and show the feasibility and effectiveness of fusing multiple classifiers through the Sugeno integral extended by ordered weighted averaging operators. In comparison with the Sugeno integral by computer simulations on the iris data and the appendicitis data show that the overall classification accuracy rate could be improved by the Sugeno integral with ordered weighted averaging operators. The experimental results further demonstrate that the proposed method performs well in comparison with other fuzzy or non-fuzzy classification methods.

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.

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