An efficient gene selection method for microarray data based on LASSO and BPSO.BACKGROUND
 To obtain the most predictive genes subsets without filtering out critical genes, a gene selection method based on least absolute shrinkage and selection operator (LASSO) and an improved binary particle swarm optimization (BPSO) is proposed in this paper.  To avoid overfitting of LASSO, the initial gene pool is divided into clusters based on their structure.  LASSO is then employed to select high predictive genes and further calculate the contribution value which indicates the genes' sensitivity to samples' classes.  With the second-level gene pool established by double filter strategy, the BPSO encoding the contribution information obtained from LASSO is improved to perform gene selection. 
LASSO
An efficient gene selection method for microarray data based on LASSO and BPSO.BACKGROUND
 To obtain the most predictive genes subsets without filtering out critical genes, a gene selection method based on least absolute shrinkage and selection operator (LASSO) and an improved binary particle swarm optimization (BPSO) is proposed in this paper.  With the second-level gene pool established by double filter strategy, the BPSO encoding the contribution information obtained from LASSO is improved to perform gene selection.  Moreover, from the perspective of the bit change probability, a new mapping function is defined to guide the updating of the particle to select the more predictive genes in the improved BPSO.


 With the compact gene pool obtained by double filter strategies, the improved BPSO could select the optimal gene subsets with high probability. 
BPSO