Abstract
BackgroundThe main goal of successful gene selection for microarray data is to find compact and predictive gene subsets which could improve the accuracy. Though a large pool of available methods exists, selecting the optimal gene subset for accurate classification is still very challenging for the diagnosis and treatment of cancer.ResultsTo 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. 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.ConclusionsWith the compact gene pool obtained by double filter strategies, the improved BPSO could select the optimal gene subsets with high probability. The experimental results on several public microarray data with extreme learning machine verify the effectiveness of the proposed method compared to the relevant methods.
Highlights
The main goal of successful gene selection for microarray data is to find compact and predictive gene subsets which could improve the accuracy
With the compact gene pool obtained by double filter strategies, the improved binary particle swarm optimization (BPSO) could select the optimal gene subsets with high probability
The classification ability of the gene subsets selected by the proposed method To verify the classification ability of the selected gene subsets, extreme learning machine (ELM) is used to perform sample classification with some gene subsets selected by the KL-IBPSO-ELM method on the five datasets
Summary
The main goal of successful gene selection for microarray data is to find compact and predictive gene subsets which could improve the accuracy. Though a large pool of available methods exists, selecting the optimal gene subset for accurate classification is still very challenging for the diagnosis and treatment of cancer. Selecting a critical gene subset could decrease the computational complexity and gene redundancy, but the development of swarm intelligence optimization algorithm offers great advantages for microarray data [4]. Due to its simple operation, fast convergence, good global search ability, the swarm intelligence optimization algorithm has been widely accepted and successfully applied to solve a lot of problems. As an efficient global search technique, particle swarm optimization (PSO) [5, 6] has been widely applied to microarray data. In [9], a combination of teaching learning-based optimization (TLBO) and particle swarm optimization was proposed to
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