Abstract
A fundamental problem in microarray analysis is to identify relevant genes from large amounts of expression data. Feature selection aims at identifying a subset of features for building robust learning models. However, finding the optimal number of features is a challenging problem, as it is a trade off between information loss when pruning excessively and noise increase when pruning is too weak. This paper presents a novel representation of genes as strings of bits and a method which automatically selects the minimum number of genes to reach a good classification accuracy on the training set. Our method first eliminates redundant features, which do not add further information for classification, then it exploits a set covering algorithm. Preliminary experimental results on public datasets confirm the intuition of the proposed method leading to high classification accuracy.
| Original language | English |
|---|---|
| Pages (from-to) | 5692-5695 |
| Number of pages | 4 |
| Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference |
| Publication status | Published - 2008 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics
Cite this
Minimum number of genes for microarray feature selection. / Baralis, Elena; Bruno, Giulia; Fiori, Alessandro.
In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2008, p. 5692-5695.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Minimum number of genes for microarray feature selection.
AU - Baralis, Elena
AU - Bruno, Giulia
AU - Fiori, Alessandro
PY - 2008
Y1 - 2008
N2 - A fundamental problem in microarray analysis is to identify relevant genes from large amounts of expression data. Feature selection aims at identifying a subset of features for building robust learning models. However, finding the optimal number of features is a challenging problem, as it is a trade off between information loss when pruning excessively and noise increase when pruning is too weak. This paper presents a novel representation of genes as strings of bits and a method which automatically selects the minimum number of genes to reach a good classification accuracy on the training set. Our method first eliminates redundant features, which do not add further information for classification, then it exploits a set covering algorithm. Preliminary experimental results on public datasets confirm the intuition of the proposed method leading to high classification accuracy.
AB - A fundamental problem in microarray analysis is to identify relevant genes from large amounts of expression data. Feature selection aims at identifying a subset of features for building robust learning models. However, finding the optimal number of features is a challenging problem, as it is a trade off between information loss when pruning excessively and noise increase when pruning is too weak. This paper presents a novel representation of genes as strings of bits and a method which automatically selects the minimum number of genes to reach a good classification accuracy on the training set. Our method first eliminates redundant features, which do not add further information for classification, then it exploits a set covering algorithm. Preliminary experimental results on public datasets confirm the intuition of the proposed method leading to high classification accuracy.
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UR - http://www.scopus.com/inward/citedby.url?scp=84903877411&partnerID=8YFLogxK
M3 - Article
C2 - 19164009
SP - 5692
EP - 5695
JO - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
SN - 1557-170X
ER -