Paper Info
Title
Feature set enhancement via hierarchical clustering for microarray classification
Abstract
A new method for gene expression classification is proposed in this paper. In a first step, the original feature set is enriched by including new features, called metagenes, produced via hierarchical clustering. In a second step, a reliable classifier is built from a wrapper feature selection process. The selection relies on two criteria: the classical classification error rate and a new reliability measure. As a result, a classifier with good predictive ability using as few features as possible to reduce the risk of overfitting is obtained. This method has been tested on three public cancer datasets: leukemia, lymphoma and colon. The proposed method has obtained interesting classification results and the experiments have confirmed the utility of both metagenes and feature ranking criterion to improve the final classifier.
Year
DOI
Venue
2011
10.1109/GENSiPS.2011.6169486
Genomic Signal Processing and Statistics
Keywords
Field
DocType
cancer,medical computing,pattern classification,pattern clustering,classical classification error rate,classifier,colon,feature set enhancement,gene expression classification,hierarchical clustering,leukemia,lymphoma,metagenes,microarray classification,overfitting risk reduction,public cancer datasets,reliability measure,wrapper feature selection process,Treelet,cancer microarray classification,feature selection,hierarchical clustering
Data mining,Feature selection,Computer science,Feature set,Artificial intelligence,Overfitting,Classifier (linguistics),Cluster analysis,Hierarchical clustering,Pattern recognition,Word error rate,Principal component analysis,Machine learning
Conference
ISSN
ISBN
Citations 
2150-3001 E-ISBN : 978-1-4673-0489-4
978-1-4673-0489-4
1
PageRank 
References 
Authors
0.38
4
4
Name
Order
Citations
PageRank
Mattia Bosio112.07
Pau Bellot Pujalte210.38
Philippe Salembier360387.65
Albert Oliveras-Vergés492.22