Instance Selection Method for Improving Graph-Based Semi-supervised Learning

2016 ◽  
pp. 565-573
Author(s):  
Hai Wang ◽  
Shao-Bo Wang ◽  
Yu-Feng Li
Keyword(s):  
Supervised Learning ◽  
Selection Method ◽  
Instance Selection

Instance selection method for improving graph-based semi-supervised learning

2018 ◽  
Vol 12 (4) ◽  
pp. 725-735 ◽  
Author(s):  
Hai Wang ◽  
Shao-Bo Wang ◽  
Yu-Feng Li
Keyword(s):  
Supervised Learning ◽  
Selection Method ◽  
Instance Selection

Choosing instance selection method using meta-learning

2014 ◽  
Author(s):  
Shayane de Oliveira Moura ◽  
Marcelo Bassani de Freitas ◽  
Halisson A. C. Cardoso ◽  
George D. C. Cavalcanti
Keyword(s):  
Selection Method ◽  
Instance Selection ◽  
Meta Learning

scholarly journals CLASSIFICATION OF HEALTHY PEOPLE AND PD PATIENTS USING TAKAGI–SUGENO FUZZY MODEL-BASED INSTANCE SELECTION AND WAVELET TRANSFORMS

2020 ◽  
Vol 20 (10) ◽  
pp. 2040039
Author(s):  
SANG-HONG LEE
Keyword(s):  
Classification Accuracy ◽  
Wavelet Transforms ◽  
Fuzzy Model ◽  
Selection Method ◽  
Healthy People ◽  
Instance Selection ◽  
Pressure Data ◽  
Foot Pressure ◽  
Before And After ◽  
Takagi Sugeno

In this study, a new instance selection method that combines the neural network with weighted fuzzy memberships (NEWFM) and Takagi–Sugeno (T–S) fuzzy model was proposed to improve the classification accuracy of healthy people and Parkinson’s disease (PD) patients. In order to evaluate the proposed instance selection for the classification accuracy of healthy people and PD patients, foot pressure data were collected from healthy people and PD patients as experimental data. This study uses wavelet transforms (WTs) to remove the noise from the foot pressure data in preprocessing step. The proposed instance selection method is an algorithm that selects instances using both weighted mean defuzzification (WMD) in the T–S fuzzy model and the confidence interval of a normal distribution used in statistics. The classification accuracy was compared before and after instance selection was applied to prove the superiority of instance selection. Classification accuracy before and after instance selection was 77.33% and 78.19%, respectively. The classification accuracy after instance selection exhibited a higher classification accuracy than that before instance selection by 0.86%. Further, McNemar’s test, which is used in statistics, was employed to show the difference in classification accuracy before and after instance selection was applied. The results of the McNemar’s test revealed that the probability of significance was smaller than 0.05, which reaffirmed that the classification accuracy was better when instance selection was applied than when instance selection was not applied. NEWFM includes the bounded sum of weighted fuzzy memberships (BSWFMs) that can easily show the differences in the graphically distinct characteristics between healthy people and PD patients. This study proposes new technique that NEWFM can detect PD patients from foot pressure data by the BSWFMs embedded in devices or systems.

scholarly journals Applying an instance selection method to an evolutionary neural classifier design

2017 ◽  
Vol 173 ◽  
pp. 012007 ◽  
Author(s):  
Dmitrii Khritonenko ◽  
Vladimir Stanovov ◽  
Eugene Semenkin
Keyword(s):  
Selection Method ◽  
Instance Selection ◽  
Classifier Design ◽  
Neural Classifier
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