Extended Particle Swarm Optimization (EPSO) for Feature Selection of High Dimensional Biomedical Data
This work addresses the challenge of handling thousands of features in gene expression data for medical diagnosis, though it appears incremental as it modifies an existing PSO method.
The paper tackles feature selection for high-dimensional biomedical data, specifically gene expression profiles for cancer diagnosis, by proposing an Extended Particle Swarm Optimization (EPSO) model that reduces processing time from an average of 95.72 seconds to 62.14 seconds and improves classification accuracy from 52-96% to 54-100% compared to standard PSO.
This paper proposes a novel Extended Particle Swarm Optimization model (EPSO) that potentially enhances the search process of PSO for optimization problem. Evidently, gene expression profiles are significantly important measurement factor in molecular biology that is used in medical diagnosis of cancer types. The challenge to certain classification methodologies for gene expression profiles lies in the thousands of features recorded for each sample. A modified Wrapper feature selection model is applied with the aim of addressing the gene classification challenge by replacing its randomness approach with EPSO and PSO respectively. EPSO is initializing the random size of the population and dividing them into two groups in order to promote the exploration and reduce the probability of falling in stagnation. Experimentally, EPSO has required less processing time to select the optimal features (average of 62.14 sec) than PSO (average of 95.72 sec). Furthermore, EPSO accuracy has provided better classification results (start from 54% to 100%) than PSO (start from 52% to 96%).