Name
Title | ||
|---|---|---|
An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data. |
Abstract | ||
|---|---|---|
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.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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1093/bioinformatics/bts602 | Bioinformatics |
Keywords | Field | DocType |
cancer classification,gene subsets,better learning performance,relevant subset,symmetrical uncertainty,small set,ensemble correlation-based gene selection,support vector machine,gene expression data,feature selection problem,machine learning,feature selection technique,gene selection | Cancer classification,Data mining,Dimensionality reduction,Feature selection,Computer science,Microarray analysis techniques,Artificial intelligence,Discriminative model,Small set,Support vector machine,Algorithm,Correlation,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 24 | 1367-4811 |
Citations | PageRank | References |
18 | 0.79 | 35 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongjun Piao | 1 | 29 | 2.37 |
| Minghao Piao | 2 | 37 | 6.30 |
| Kiejung Park | 3 | 43 | 4.42 |
| Keun Ho Ryu | 4 | 883 | 85.61 |