A Compact Belief Rule-Based Classification System with Evidential Clustering

2018 ◽  
pp. 137-145
Author(s):  
Lianmeng Jiao ◽  
Xiaojiao Geng ◽  
Quan Pan
Keyword(s):  
Classification System ◽  
Rule Based

Deep Fuzzy Rule-based Classification System with Improved Wang-Mendel Method

2021 ◽  
pp. 1-1
Author(s):  
Yuangang Wang ◽  
Haoran Liu ◽  
Wenjuan Jia ◽  
Shuo Guan ◽  
Xiaodong Liu ◽  
...  
Keyword(s):  
Classification System ◽  
Fuzzy Rule ◽  
Rule Based

scholarly journals Compact Belief Rule Base Learning for Classification with Evidential Clustering

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 443 ◽  
Author(s):  
Lianmeng Jiao ◽  
Xiaojiao Geng ◽  
Quan Pan
Keyword(s):  
Classification System ◽  
Optimization Procedure ◽  
Classification Model ◽  
Rule Base ◽  
Data Sets ◽  
Classification Problems ◽  
Ecm Algorithm ◽  
Rule Based ◽  
Belief Rule Base ◽  
Belief Function Theory

The belief rule-based classification system (BRBCS) is a promising technique for addressing different types of uncertainty in complex classification problems, by introducing the belief function theory into the classical fuzzy rule-based classification system. However, in the BRBCS, high numbers of instances and features generally induce a belief rule base (BRB) with large size, which degrades the interpretability of the classification model for big data sets. In this paper, a BRB learning method based on the evidential C-means clustering (ECM) algorithm is proposed to efficiently design a compact belief rule-based classification system (CBRBCS). First, a supervised version of the ECM algorithm is designed by means of weighted product-space clustering to partition the training set with the goals of obtaining both good inter-cluster separability and inner-cluster pureness. Then, a systematic method is developed to construct belief rules based on the obtained credal partitions. Finally, an evidential partition entropy-based optimization procedure is designed to get a compact BRB with a better trade-off between accuracy and interpretability. The key benefit of the proposed CBRBCS is that it can provide a more interpretable classification model on the premise of comparative accuracy. Experiments based on synthetic and real data sets have been conducted to evaluate the classification accuracy and interpretability of the proposal.

Modified Fuzzy Rule-Based Classification System for Early Warning of Student Learning

2019 ◽  
Vol 48 (3) ◽  
pp. 385-406 ◽  
Author(s):  
Qun Zhao ◽  
Jin-Long Wang ◽  
Tsang-Long Pao ◽  
Li-Yu Wang
Keyword(s):  
Student Learning ◽  
Classification System ◽  
Prediction Accuracy ◽  
Confusion Matrix ◽  
Great Influence ◽  
Fuzzy Rule ◽  
Two Dimensions ◽  
Learning Performance ◽  
Rule Based

This study uses the log data from Moodle learning management system for predicting student learning performance in the first third of a semester. Since the quality of the data has great influence on the accuracy of machine learning, five major data transmission methods are used to enhance data quality of log file in the data preprocessing stage. Furthermore, the modified FRBCS-CHI (fuzzy rule-based classification system using Chi's technique) algorithm, based on the weighted consequence, is proposed to improve the prediction accuracy of classification. Thereafter, the confusion matrix with two dimensions is employed to illustrate the prediction results, such as false positives, false negatives, true positives, and true negatives, which are further used to produce the parameters of prediction performance, including the precision rate, the recall rate, and the F-measure. From the results of experiment, the proposed modified FRBCS-CHI method will have higher prediction accuracy than the original FRBCS-CHI method.

Fuzzy rule-based classification system using multi-population quantum evolutionary algorithm with contradictory rule reconstruction

2019 ◽  
Vol 49 (11) ◽  
pp. 4007-4021 ◽  
Author(s):  
YuXian Zhang ◽  
XiaoYi Qian ◽  
Jianhui Wang ◽  
Mohammed Gendeel
Keyword(s):  
Evolutionary Algorithm ◽  
Classification System ◽  
Fuzzy Rule ◽  
Rule Based

scholarly journals IVTURS: A Linguistic Fuzzy Rule-Based Classification System Based On a New Interval-Valued Fuzzy Reasoning Method With Tuning and Rule Selection

2013 ◽  
Vol 21 (3) ◽  
pp. 399-411 ◽  
Author(s):  
Jose Antonio Sanz ◽  
Alberto Fernandez ◽  
Humberto Bustince ◽  
Francisco Herrera
Keyword(s):  
Classification System ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Rule Based ◽  
Rule Selection ◽  
Interval Valued
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