Pre-processing for noise detection in gene expression classification data
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
---|---|
Data(s) |
26/03/2012
26/03/2012
2009
|
Resumo |
Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data. São Paulo State Research Foundation (FAPESP) CNPq |
Identificador |
Journal of the Brazilian Computer Society, v.15, n.1, p.3-11, 2009 0104-6500 http://producao.usp.br/handle/BDPI/11824 10.1590/S0104-65002009000100002 http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002009000100002 |
Idioma(s) |
eng |
Publicador |
Sociedade Brasileira de Computação |
Relação |
Journal of the Brazilian Computer Society |
Direitos |
openAccess Copyright Sociedade Brasileira de Computação |
Palavras-Chave | #Noise detection #Machine learning #Distance-based techniques #Gene expression analysis |
Tipo |
article original article publishedVersion |