Gene Expression Microarray Data Classification based on PCA and Cuttlefish Algorithm

The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the prospect patterns directly and accurately. One of the necessary strategies for distinguishing and screening out the most relevant features is Feature Selection (FS). However, the increasing feature dimensions and small sample size in microarray datasets pose a significant challenge to most existing algorithms. To overcome this issue, we propose a novel method based on Principle Component Analysis (PCA) and Cuttlefish Algorithm (CFA), which is a recent bio-inspired feature selection algorithm. The critical characteristic of the PCA algorithm is that it is less sensitive to noise and requires less memory and capacity. Furthermore, adopting the PCA approach before using CFA minimises the search space within CFA, which speeds up determining the best subset of features while reducing the computational cost. To assess the performance of the proposed method, three publicly available microarray datasets are utilized in the experimental studies using a Linear Discriminant Analysis classifier. Experimental results showed that PCA with CFA significantly outperforms the state-of-art feature selection methods.