scholarly journals Corrigendum to “Effective feature selection based on Fisher Ratio for snoring recognition using different validation methods” [Appl. Acoust. 185 (2022) 108429:1–8]

2022 ◽  
Vol 186 ◽  
pp. 108483
Author(s):  
Xiaoran Sun ◽  
Jianxin Peng ◽  
Xiaowen Zhang ◽  
Lijuan Song
Keyword(s):  
Feature Selection ◽  
Validation Methods ◽  
Fisher Ratio

Effective feature selection based on Fisher Ratio for snoring recognition using different validation methods

2022 ◽  
Vol 185 ◽  
pp. 108429
Author(s):  
Xiaoran Sun ◽  
Jianxin Peng ◽  
Xiaowen Zhang ◽  
Lijuan Song
Keyword(s):  
Feature Selection ◽  
Validation Methods ◽  
Fisher Ratio

A Research of Speaker Recognition Based on VQ and MFCC

2014 ◽  
Vol 644-650 ◽  
pp. 4325-4329 ◽  
Author(s):  
Chen Chen Huang ◽  
Wei Gong ◽  
Wen Long Fu ◽  
Dong Yu Feng
Keyword(s):  
Feature Selection ◽  
Speaker Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Test Results ◽  
Selection Methods ◽  
Accuracy Rate ◽  
Fisher Ratio ◽  
Degree Of Differentiation

—Feature extraction is a very important part in speaker recognition system. We proposed and implemented a speaker recognition algorithm based on the VQ and weighted fisher ratio of MFCC. To evaluate performance of this algorithm, we built a small speaker recognition system based on the MATLAB. Compared with the traditional feature selection methods, the characteristic vector obtained via this algorithm has the greatest degree of differentiation in the same dimension. According to the test results, the speaker recognition algorithm we proposed in this paper, can significantly increase the accuracy rate of training and recognition, and reduce the data required by calculation, in the case of keeping a higher recognition rate.

scholarly journals Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Atiyeh Mortazavi ◽  
Mohammad Hossein Moattar
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Cooperative Game ◽  
Microarray Data ◽  
Second Phase ◽  
Data Sets ◽  
Forward Selection ◽  
Selection Scheme ◽  
Fisher Ratio ◽  
Low Efficiency

High dimensionality of microarray data sets may lead to low efficiency and overfitting. In this paper, a multiphase cooperative game theoretic feature selection approach is proposed for microarray data classification. In the first phase, due to high dimension of microarray data sets, the features are reduced using one of the two filter-based feature selection methods, namely, mutual information and Fisher ratio. In the second phase, Shapley index is used to evaluate the power of each feature. The main innovation of the proposed approach is to employ Qualitative Mutual Information (QMI) for this purpose. The idea of Qualitative Mutual Information causes the selected features to have more stability and this stability helps to deal with the problem of data imbalance and scarcity. In the third phase, a forward selection scheme is applied which uses a scoring function to weight each feature. The performance of the proposed method is compared with other popular feature selection algorithms such as Fisher ratio, minimum redundancy maximum relevance, and previous works on cooperative game based feature selection. The average classification accuracy on eleven microarray data sets shows that the proposed method improves both average accuracy and average stability compared to other approaches.

Feature Selection Strategies and Perceptual Expertise in Configuration Search Tasks

2011 ◽  
Author(s):  
Lindsey M. Kitchell ◽  
Francisco J. Parada ◽  
Brandi L. Emerick ◽  
Tom A. Busey
Keyword(s):  
Feature Selection ◽  
Perceptual Expertise ◽  
Search Tasks ◽  
Selection Strategies

BLIND FEATURE SELECTION AND EXTRACTION IN A 3D IMAGE CUBE

2012 ◽  
Vol 19 (2) ◽  
pp. 97-111 ◽  
Author(s):  
Muhammad Ahmad ◽  
Syungyoung Lee ◽  
Ihsan Ul Haq ◽  
Qaisar Mushtaq
Keyword(s):  
Feature Selection ◽  
3D Image ◽  
Image Cube
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