Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44066
Title: Reducing features from pejibaye palm DNA marker for an efficient classification
Authors: Travieso, Carlos M. 
Alonso, Jesús B. 
Ferrer, Miguel A. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Feature reduction Discriminative Common Vector , Independent Component Analysis , Principal Component Analysis , supervised classification , DNA markers
Issue Date: 2010
Publisher: 0302-9743
Journal: Lecture Notes in Computer Science 
Conference: International Conference on Nonlinear Speech Processing (NOLISP 2009) 
International Conference on Nonlinear Speech Processing, NOLISP 2009 
Abstract: This present work presents different feature reduction methods, applied to Deoxyribonucleic Acid (DNA) marker, and in order to identify a success of 100% based on Discriminate Common Vectors (DCV), Principal Component Analysis (PCA), and Independent Component Analysis (ICA) using as classifiers Support Vector Machines (SVM) and Artificial Neural Networks. In particular, the biochemical parameterization has 89 Random Amplified polymorphic DNA (RADPS) markers of Pejibaye palm landraces, and it has been reduced from 89 to a 3 characteristics, for the best method using ICA. The interest of this application is due to feature reduction and therefore, the reduction of computational load time versus the use of all features. This method allows having a faster supervised classification system for the process of the plant certification with origin denomination. Therefore, this system can be transferred to voice applications in order to reduce load time, keeping or improving the success rates.
URI: http://hdl.handle.net/10553/44066
ISBN: 978-3-642-11508-0
364211508X
ISSN: 0302-9743
DOI: 10.1007/978-3-642-11509-7_20
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 5933 LNAI, p. 152-162
Appears in Collections:Actas de congresos
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