Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Text classification based on partial least square analysis
Proceedings of the 2007 ACM symposium on Applied computing
Orthogonal projection weights in dimension reduction based on Partial Least Squares
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
On the number of partial least squares components in dimension reduction for tumor classification
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Model selection for partial least squares based dimension reduction
Pattern Recognition Letters
International Journal of Data Mining and Bioinformatics
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It is hard to analyse gene expression data which has only a few observations but with thousands of measured genes. Partial Least Squares based Dimension Reduction (PLSDR) is superior for handling such high dimensional problems, but irrelevant features will introduce errors into the dimension reduction process. Here, feature selection is applied to filter the data and an algorithm named PLSDRg is described by integrating PLSDR with gene elimination, which is performed by the indication of t-statistic scores on standardised probes. Experimental results on six microarray data sets show that PLSDRg is effective and reliable to improve generalisation performance of classifiers.