An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data.
BACKGROUND
Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data pose a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target.


RESULTS
In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine. In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature.
Department of Electrical and Computer Engineering, Chungbuk National University, Chungbuk, Korea.
Yongjun
Y
Piao
Minghao
M
Piao
Kiejung
K
Park
Keun Ho
KH
Ryu
D000465:Algorithms; D001185:Artificial Intelligence; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D060388:Support Vector Machine