Application of Feature Subset Selection Methods on Classifiers Comprehensibility for Bio-Medical Datasets

2016 ◽  
pp. 38-43 ◽  
Author(s):  
Syed Imran Ali ◽  
Byeong Ho Kang ◽  
Sungyoung Lee
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Selection Methods

scholarly journals Emotional Classification of Acoustic Information With Optimal Feature Subset Selection Methods

2018 ◽  
Vol 7 (2.32) ◽  
pp. 39
Author(s):  
Dr Swarna Kuchibhotla ◽  
Mr Niranjan M.S.R
Keyword(s):  
High Efficiency ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Computational Time ◽  
Feature Subset ◽  
Selection Methods ◽  
Forward Selection ◽  
Optimal Feature Subset ◽  
Optimal Feature

This paper mainly focuses on classification of various Acoustic emotional corpora with frequency domain features using feature subset selection methods. The emotional speech samples are classified into neutral,  happy, fear , anger,  disgust and sad  states by using properties of statistics  of spectral features estimated from Berlin and Spanish emotional utterances. The Sequential Forward Selection(SFS) and Sequential Floating Forward Selection(SFFS)feature subset selection algorithms are  for extracting more informative features. The number of speech emotional samples available for training is smaller than that of the number of features extracted from the speech sample in both Berlin and Spanish corpora which is called curse of dimensionality. Because of this  feature vector of high dimensionality the efficiency of the classifier decreases and at the same time the computational time also increases. For additional  improvement in the efficiency of the classifier  a subset of  features which are optimal is needed and is obtained by using feature subset selection methods. This will enhances the performance of the system with high efficiency and lower computation time. The classifier used in this work is the standard K Nearest Neighbour (KNN) Classifier. Experimental evaluation   proved  that the performance of the classifier is enhanced with SFFS because it vanishes the nesting effect suffered by SFS. The results also showed that an optimal feature subset is a better choice for classification rather than full feature set.  

Sign in / Sign up

Export Citation Format

Share Document