Feature Selection for High-Dimensional Genomic Microarray Data
Abstract
We report on the successful application of feature selection methods to a classification problem in molecular biology involving only 72 data points in a 7130 dimensional space. Our approach is a hybrid of filter and wrapper approaches to feature selection. We make use of a sequence of simple filters, culminating in Koller and Sahami's (1996) Markov Blanket filter, to decide on particular feature subsets for each subset cardinality. We compare between the resulting subset cardinalities using cross validation. The paper also investigates regularization methods as an alternative to feature selection, showing that feature selection methods are preferable in this problem.
Cite
Text
Xing et al. "Feature Selection for High-Dimensional Genomic Microarray Data." International Conference on Machine Learning, 2001.Markdown
[Xing et al. "Feature Selection for High-Dimensional Genomic Microarray Data." International Conference on Machine Learning, 2001.](https://mlanthology.org/icml/2001/xing2001icml-feature/)BibTeX
@inproceedings{xing2001icml-feature,
title = {{Feature Selection for High-Dimensional Genomic Microarray Data}},
author = {Xing, Eric P. and Jordan, Michael I. and Karp, Richard M.},
booktitle = {International Conference on Machine Learning},
year = {2001},
pages = {601-608},
url = {https://mlanthology.org/icml/2001/xing2001icml-feature/}
}