PLS Dimension Reduction for Classification with Microarray Data
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| 008 | 161128e20041123xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.2202/1544-6115.1075 |2 doi |
| 035 | |a (NATIONALLICENCE)gruyter-10.2202/1544-6115.1075 | ||
| 100 | 1 | |a Boulesteix |D Anne-Laure |u Department of Statistics, University of Munich | |
| 245 | 1 | 0 | |a PLS Dimension Reduction for Classification with Microarray Data |h [Elektronische Daten] |c [Anne-Laure Boulesteix] |
| 520 | 3 | |a Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new components is compared with some of the best state-of-the-art classification methods. Moreover, a boosting algorithm is applied to this classification method. In addition, a simple procedure to choose the number of PLS components is suggested. The connection between PLS dimension reduction and gene selection is examined and a property of the first PLS component for binary classification is proved. In addition, we show how PLS can be used for data visualization using real data. The whole study is based on 9 real microarray cancer data sets. | |
| 540 | |a ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston | ||
| 690 | 7 | |a Microarrays |2 nationallicence | |
| 690 | 7 | |a Multivariate Analysis |2 nationallicence | |
| 690 | 7 | |a partial least squares |2 nationallicence | |
| 690 | 7 | |a feature extraction |2 nationallicence | |
| 690 | 7 | |a variable selection |2 nationallicence | |
| 690 | 7 | |a boosting |2 nationallicence | |
| 690 | 7 | |a gene expression |2 nationallicence | |
| 690 | 7 | |a discriminant analysis |2 nationallicence | |
| 690 | 7 | |a supervised learning |2 nationallicence | |
| 773 | 0 | |t Statistical Applications in Genetics and Molecular Biology |d De Gruyter |g 3/1(2004-11-23), 1-30 |q 3:1<1 |1 2004 |2 3 |o sagmb | |
| 856 | 4 | 0 | |u https://doi.org/10.2202/1544-6115.1075 |q text/html |z Onlinezugriff via DOI |
| 908 | |D 1 |a research article |2 jats | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.2202/1544-6115.1075 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 100 |E 1- |a Boulesteix |D Anne-Laure |u Department of Statistics, University of Munich | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Statistical Applications in Genetics and Molecular Biology |d De Gruyter |g 3/1(2004-11-23), 1-30 |q 3:1<1 |1 2004 |2 3 |o sagmb | ||
| 900 | 7 | |b CC0 |u http://creativecommons.org/publicdomain/zero/1.0 |2 nationallicence | |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-gruyter | ||