A fuzzy learning model for membership function estimation and pattern classification

2002 ◽  
Author(s):  
Fu-Lai Chung ◽  
Tong Lee
Keyword(s):  
Pattern Classification ◽  
Membership Function ◽  
Learning Model ◽  
Function Estimation ◽  
Fuzzy Learning

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

Interval-valued membership function estimation for fuzzy modeling

2019 ◽  
Vol 361 ◽  
pp. 101-113 ◽  
Author(s):  
Moufid Bouhentala ◽  
Mouna Ghanai ◽  
Kheireddine Chafaa
Keyword(s):  
Membership Function ◽  
Fuzzy Modeling ◽  
Function Estimation ◽  
Interval Valued

Multitask Fuzzy Learning with Rule Weight

2013 ◽  
Vol 774-776 ◽  
pp. 1883-1886
Author(s):  
Zhen Xing Li ◽  
Wei Hua Li
Keyword(s):  
Error Correction ◽  
Membership Function ◽  
Fuzzy Set ◽  
Multitask Learning ◽  
Learning System ◽  
Experimental Results ◽  
Learning Machines ◽  
Fuzzy Learning

In fuzzy learning system based on rule weight, certainty grade, denoted by membership function of fuzzy set, defines how close a rule to a classification. In this system, several rules can correspond to same classification. But it cannot reflect the changing while training several tasks simultaneously. In this paper, we propose multitask fuzzy learning based on error-correction, and define belonging grade to show how much a sample belongs to a rule. Experimental results demonstrate efficiency of multitask fuzzy learning, and multitask learning could help to improve learning machines prediction.

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