Comparison among dimensionality reduction techniques based on Random Projection for cancer classification. (December 2016)

• Record Type: Journal Article
• Authors: Xie, Haozhe; Li, Jie; Zhang, Qiaosheng; Wang, Yadong
• Is Part Of: Computational biology and chemistry. Volume 65(2016)
• Page Start: 165
• Page End: 172
• Publication Date: 2016-12
• Subjects: Random Projection -- Dimensionality reduction -- Classification -- Breast cancer; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.09.010
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. However, the performance of RP is usually lower. We attempt to improve classification accuracy of RP through combining other reduction dimension methods such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Feature Selection (FS). We compared classification accuracy and running time of different combination methods on three microarray datasets and a simulation dataset. Experimental results show a remarkable improvement of 14.77% in classification accuracy of FS followed by RP compared to RP on BC-TCGA dataset. LDA followed by RP also helps RP to yield a more discriminative subspace with an increase of 13.65% on classification accuracy on the same dataset. FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.