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Paper Information

Journal:   JOURNAL OF ELECTRONIC AND CYBER DEFENCE   FALL 2015 , Volume 3 , Number 3 (11); Page(s) 1 To 7.
 
Paper: 

AN OPTIMIZED UNSUPERVISED FEATURE SELECTION ALGORITHM

 
 
Author(s):  KAKAEI MOTLAGH H.R., MOLLAZADEH GOLMAHALEH M.*, TEYMOURPOUR B.
 
* IMAM HOSSEIN UNIVERSITY
 
Abstract: 

Choosing a feature vector for maximizing the success of a classifier machine is very effective. In this paper, using a combination of different methods to calculate the core function, an unsupervised feature selection algorithm improvement has been proposed. Feature vector obtained by the proposed algorithm, will maximizes output accuracy of back propagation neural network classifier. In this paper we used case study of standard encoding of images compressed by alternate method and uncompressed images classifying based on their relative bit stream. Standards for classifications are JPEG and JPEG2000 and for uncompressed images is TIFF format. Using this feature vector obtained by the proposed algorithm, classifier accuracy will be about 98%.

 
Keyword(s): FEATURE VECTOR, FEATURE VECTOR SELECTION, NEURAL NETWORK, CLASSIFICATION, IMAGE COMPRESSING STANDARD
 
 
References: 
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Click to Cite.
APA: Copy

KAKAEI MOTLAGH, H., & MOLLAZADEH GOLMAHALEH, M., & TEYMOURPOUR, B. (2015). AN OPTIMIZED UNSUPERVISED FEATURE SELECTION ALGORITHM. JOURNAL OF ELECTRONIC AND CYBER DEFENCE, 3(3 (11)), 1-7. https://www.sid.ir/en/journal/ViewPaper.aspx?id=509134



Vancouver: Copy

KAKAEI MOTLAGH H.R., MOLLAZADEH GOLMAHALEH M., TEYMOURPOUR B.. AN OPTIMIZED UNSUPERVISED FEATURE SELECTION ALGORITHM. JOURNAL OF ELECTRONIC AND CYBER DEFENCE. 2015 [cited 2021May07];3(3 (11)):1-7. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=509134



IEEE: Copy

KAKAEI MOTLAGH, H., MOLLAZADEH GOLMAHALEH, M., TEYMOURPOUR, B., 2015. AN OPTIMIZED UNSUPERVISED FEATURE SELECTION ALGORITHM. JOURNAL OF ELECTRONIC AND CYBER DEFENCE, [online] 3(3 (11)), pp.1-7. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=509134.



 
 
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