Feature clustering based support vector machine recursive feature elimination for gene selection

In a DNA microarray dataset, gene expression data often has a huge number of features(which are referred to as genes) versus a small size of samples. With the development of DNA microarray technology, the number of dimensions increases even faster than before, which could lead to the problem of the...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2018-03, Vol.48 (3), p.594-607
Main Authors: Huang, Xiaojuan, Zhang, Li, Wang, Bangjun, Li, Fanzhang, Zhang, Zhao
Format: Article
Language:English
Subjects:
Artificial Intelligence
Classification
Clustering
Complexity
Computation
Computer Science
Deoxyribonucleic acid
DNA
Gene expression
Genes
Machines
Manufacturing
Mechanical Engineering
Processes
Recursive methods
Redundancy
Support vector machines
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Summary:In a DNA microarray dataset, gene expression data often has a huge number of features(which are referred to as genes) versus a small size of samples. With the development of DNA microarray technology, the number of dimensions increases even faster than before, which could lead to the problem of the curse of dimensionality. To get good classification performance, it is necessary to preprocess the gene expression data. Support vector machine recursive feature elimination (SVM-RFE) is a classical method for gene selection. However, SVM-RFE suffers from high computational complexity. To remedy it, this paper enhances SVM-RFE for gene selection by incorporating feature clustering, called feature clustering SVM-RFE (FCSVM-RFE). The proposed method first performs gene selection roughly and then ranks the selected genes. First, a clustering algorithm is used to cluster genes into gene groups, in each which genes have similar expression profile. Then, a representative gene is found to represent a gene group. By doing so, we can obtain a representative gene set. Then, SVM-RFE is applied to rank these representative genes. FCSVM-RFE can reduce the computational complexity and the redundancy among genes. Experiments on seven public gene expression datasets show that FCSVM-RFE can achieve a better classification performance and lower computational complexity when compared with the state-the-art-of methods, such as SVM-RFE.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-017-0992-2