scholarly journals Performance Optimization of a Fuzzy Entropy Based Feature Selection and Classification Framework

2018 ◽  
Author(s):  
Zixiao Shen ◽  
Xin Chen ◽  
Jon Garibaldi
Keyword(s):  
Feature Selection ◽  
Performance Optimization ◽  
Fuzzy Entropy ◽  
Classification Framework

FEATURE SELECTION BASED ON MINIMUM ERROR MINIMAX PROBABILITY MACHINE

2007 ◽  
Vol 21 (08) ◽  
pp. 1279-1292 ◽  
Author(s):  
ZENGLIN XU ◽  
IRWIN KING ◽  
MICHAEL R. LYU
Keyword(s):  
Feature Selection ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Feature Subset ◽  
Minimum Error ◽  
Automatic Balance ◽  
Classification Framework ◽  
Data Partitions ◽  
Minimax Probability Machine ◽  
Optimal Feature Subset

Feature selection is an important task in pattern recognition. Support Vector Machine (SVM) and Minimax Probability Machine (MPM) have been successfully used as the classification framework for feature selection. However, these paradigms cannot automatically control the balance between prediction accuracy and the number of selected features. In addition, the selected feature subsets are also not stable in different data partitions. Minimum Error Minimax Probability Machine (MEMPM) has been proposed for classification recently. In this paper, we outline MEMPM to select the optimal feature subset with good stability and automatic balance between prediction accuracy and the size of feature subset. The experiments against feature selection with SVM and MPM show the advantages of the proposed MEMPM formulation in stability and automatic balance between the feature subset size and the prediction accuracy.

scholarly journals A contemporary feature selection and classification framework for imbalanced biomedical datasets

2018 ◽  
Vol 19 (3) ◽  
pp. 191-198 ◽  
Author(s):  
Thulasi Bikku ◽  
Sambasiva Rao Nandam ◽  
Ananda Rao Akepogu
Keyword(s):  
Feature Selection ◽  
Classification Framework

An efficient fuzzy classifier with feature selection based on fuzzy entropy

2001 ◽  
Vol 31 (3) ◽  
pp. 426-432 ◽  
Author(s):  
Hahn-Ming Lee ◽  
Chih-Ming Chen ◽  
Jyh-Ming Chen ◽  
Yu-Lu Jou
Keyword(s):  
Feature Selection ◽  
Fuzzy Entropy ◽  
Fuzzy Classifier

scholarly journals Pain Detection/Classification Framework including Face Recognition based on the Analysis of Facial Expressions for E-Health Systems

2020 ◽  
Vol 18 (1) ◽  
pp. 125-132
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Face Recognition ◽  
Pain Intensity ◽  
Elderly People ◽  
Facial Expressions ◽  
Intensity Scale ◽  
Classification Framework ◽  
Pain Detection ◽  
Patients With Special Needs

Facial expressions can demonstrate the presence and degree of pain of humans, which is a vital topic in E-healthcare domain specially for elderly people or patients with special needs. This paper presents a framework for pain detection, pain classification, and face recognition using feature extraction, feature selection, and classification techniques. Pain intensity is measured by Prkachin and Solomon pain intensity scale. Experimental results showed that the proposed framework is a promising one compared with previously works. It achieves 91% accuracy in pain detection, 99.89% accuracy in face recognition, and 78%, 92%, 88% accuracy, respectively, for all levels of pain classification

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