Comparison among dimensionality reduction techniques based on Random Projection for cancer classification
This work addresses the need for efficient dimensionality reduction in big genomics data for cancer classification, but it is incremental as it combines existing methods.
The paper tackled the problem of low classification accuracy in Random Projection (RP) for cancer classification by combining RP with other dimensionality reduction methods like PCA, LDA, and Feature Selection (FS), resulting in improvements such as a 14.77% increase in accuracy with FS followed by RP on the BC-TCGA dataset.
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.