TY - GEN
T1 - Feature selection using multi-layer perceptron in HIV-1 protease cleavage data
AU - Kim, Gilhan
AU - Kim, Yeonjoo
AU - Kim, Hyeoncheol
PY - 2008
Y1 - 2008
N2 - Recently, several machine learning approaches have been applied to modeling of the specificity for HIV-1 protease cleavage domain. However, HIV-1 protease cleavage domain with high dimensionality and small number of samples could misguide classification modeling and its interpretation. Thus, a method to select a smaller number of relevant features is required. Appropriate feature selection could eliminate irrelevant and redundant features, and thus, improves prediction performance and provides faster and more cost-effective models. As a result, we can gain deeper insight about dataset. In this paper, we introduce a new feature selection method, called FS-MLP, that extracts relevant features using multi-layered perceptron learning. With the method, we could extract a set of effective features in a multi-variate and non-linear way. Our experimental results on three types of artificial datasets and HIV-1 protease cleavage dataset show that performance of the FS-MLP is higher than other methods.
AB - Recently, several machine learning approaches have been applied to modeling of the specificity for HIV-1 protease cleavage domain. However, HIV-1 protease cleavage domain with high dimensionality and small number of samples could misguide classification modeling and its interpretation. Thus, a method to select a smaller number of relevant features is required. Appropriate feature selection could eliminate irrelevant and redundant features, and thus, improves prediction performance and provides faster and more cost-effective models. As a result, we can gain deeper insight about dataset. In this paper, we introduce a new feature selection method, called FS-MLP, that extracts relevant features using multi-layered perceptron learning. With the method, we could extract a set of effective features in a multi-variate and non-linear way. Our experimental results on three types of artificial datasets and HIV-1 protease cleavage dataset show that performance of the FS-MLP is higher than other methods.
UR - http://www.scopus.com/inward/record.url?scp=51549088151&partnerID=8YFLogxK
U2 - 10.1109/BMEI.2008.169
DO - 10.1109/BMEI.2008.169
M3 - Conference contribution
AN - SCOPUS:51549088151
SN - 9780769531182
T3 - BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008
SP - 279
EP - 283
BT - BioMedical Engineering and Informatics
T2 - BioMedical Engineering and Informatics: New Development and the Future - 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008
Y2 - 27 May 2008 through 30 May 2008
ER -