Deep Takagi–Sugeno–Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules

2018 ◽  
Vol 26 (3) ◽  
pp. 1535-1549 ◽  
Author(s):  
Yuanpeng Zhang ◽  
Hisao Ishibuchi ◽  
Shitong Wang
Keyword(s):  
Fuzzy Rules ◽  
Fuzzy Classifier ◽  
Takagi Sugeno ◽  
Linguistic Fuzzy Rules

scholarly journals Interpretable Recognition for Dementia Using Brain Images

2021 ◽  
Vol 15 ◽  
Author(s):  
Xinjian Song ◽  
Feng Gu ◽  
Xiude Wang ◽  
Songhua Ma ◽  
Li Wang
Keyword(s):  
Recognition Performance ◽  
Fuzzy Rule ◽  
Classification Performance ◽  
Fuzzy Rules ◽  
High Dimensional ◽  
Great Success ◽  
Fuzzy Classifier ◽  
Brain Images ◽  
Fuzzy Classifiers ◽  
Takagi Sugeno

Machine learning-based models are widely used for neuroimage-based dementia recognition and achieve great success. However, most models omit the interpretability that is a very important factor regarding the confidence of a model. Takagi–Sugeno–Kang (TSK) fuzzy classifiers as the high interpretability and promising classification performance have widely used in many scenarios. TSK fuzzy classifier can generate interpretable fuzzy rules showing the reasoning process. However, when facing high-dimensional data, the antecedent become complex which may reduce the interpretability. In this study, to keep the antecedent of fuzzy rule concise, we introduce the subspace clustering technique and use it for antecedent learning. Experimental results show that the used model can generate promising recognition performance as well as concise fuzzy rules.

AMI Screening Using Linguistic Fuzzy Rules

2010 ◽  
Vol 36 (2) ◽  
pp. 463-473 ◽  
Author(s):  
Raja Noor Ainon ◽  
Awang M. Bulgiba ◽  
Adel Lahsasna
Keyword(s):  
Fuzzy Rules ◽  
Linguistic Fuzzy Rules

scholarly journals A New Opinion Mining Method based on Fuzzy Classifier and Particle Swarm Optimization (PSO) Algorithm

2018 ◽  
Vol 18 (2) ◽  
pp. 36-50
Author(s):  
Samira Bordbar ◽  
Pirooz Shamsinejad
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Opinion Mining ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Fuzzy Rules ◽  
Training Data ◽  
Fuzzy Classifier ◽  
Swarm Optimization ◽  
Main Challenge

Abstract Opinion Mining or Sentiment Analysis is the task of extracting people final opinion about something through their unstructured sentiments. The Opinion Mining process is as follows: first, product features which are most important to a user are extracted from his/her comments. Then, sentiments will be emotionally classified using their emotional implications. In this paper we propose an opinion classification method based on Fuzzy Logic. Up to now, a few methods have taken advantage of fuzzy logic in opinion classification and all of them have imported fuzzy rules into system as background knowledge. But the main challenge here is finding the fuzzy rules. Our contribution is to automatically extract fuzzy rules and their parameters from training data. Here we have used the Particle Swarm Optimization (PSO) algorithm to extract fuzzy rules from training data. Also, for better results we have devised a mutation-based PSO. All proposed methods have been implemented and tested on relevant data. Results confirm that our method can reach better accuracy than current state of the art methods in this domain.

A fuzzy inference method based on association rule analysis with application to river flood forecasting

2012 ◽  
Vol 66 (10) ◽  
pp. 2090-2098 ◽  
Author(s):  
Chi Zhang ◽  
Yilun Wang ◽  
Lili Zhang ◽  
Huicheng Zhou
Keyword(s):  
Association Rule ◽  
Fuzzy Inference ◽  
Fuzzy Reasoning ◽  
Flood Forecasting ◽  
Fuzzy Rules ◽  
Computationally Efficient ◽  
River Flood ◽  
Input Parameters ◽  
Rule Analysis ◽  
Takagi Sugeno

In this paper, a computationally efficient version of the widely used Takagi-Sugeno (T-S) fuzzy reasoning method is proposed, and applied to river flood forecasting. It is well known that the number of fuzzy rules of traditional fuzzy reasoning methods exponentially increases as the number of input parameters increases, often causing prohibitive computational burden. The proposed method greatly reduces the number of fuzzy rules by making use of the association rule analysis on historical data, and therefore achieves computational efficiency for the cases of a large number of input parameters. In the end, we apply this new method to a case study of river flood forecasting, which demonstrates that the proposed fuzzy reasoning engine can achieve better prediction accuracy than the widely used Muskingum–Cunge scheme.

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