A hybrid feature selection algorithm using simplified swarm optimization for body fat prediction.

BACKGROUND AND OBJECTIVES: Obesity is one of the chronic diseases that seriously threaten people's health outcomes globally. Since the prevalence of obesity is increasing among people of all ages, measuring the body fat percentages is vital before treatment. However, the body fat percentage cannot be accurately measured by weighing. While many devices are commonly used to measure the body fat percentage, these devices are expensive and depend on complex instruments. Therefore, more practical and cost-effective solutions are desired to measure body fat accurately. This study presents a hybrid feature selection method based on a VIKOR-based multi-filter ensemble technique (VMFET) and an improved simplified swarm optimization (iSSO) to predict the body fat percentage with low prediction error. METHODS: The study followed a two-phase process. First, VMFET was used to aggregate the statistical outcomesof individual filters to filter the most informative features from the original dataset. Then, the selected features are applied to the next phase. Second, iSSO was tailored with a biased random initialization scheme, effect-based feature pruning scheme, and multiple linear regression as a wrapper method to improve the prediction performance and select the optimal feature subset. STUDIES RESULTS: Extensive experiments were performed using nine datasets to verify the performance of the proposed method empirically, and the corresponding results were compared with up-to-date studies. CONCLUSION: The statistical results demonstrated that the proposed method offers a promising and effective tool for predicting body fat. SIGNIFICANCE: The hybrid feature selection model can enhance prediction accuracy and lower prediction error.

Accelerated Simplified Swarm Optimization with Exploitation Search Scheme for Data Clustering.

Data clustering is commonly employed in many disciplines. The aim of clustering is to partition a set of data into clusters, in which objects within the same cluster are similar and dissimilar to other objects that belong to different clusters. Over the past decade, the evolutionary algorithm has been commonly used to solve clustering problems. This study presents a novel algorithm based on simplified swarm optimization, an emerging population-based stochastic optimization approach with the advantages of simplicity, efficiency, and flexibility. This approach combines variable vibrating search (VVS) and rapid centralized strategy (RCS) in dealing with clustering problem. VVS is an exploitation search scheme that can refine the quality of solutions by searching the extreme points nearby the global best position. RCS is developed to accelerate the convergence rate of the algorithm by using the arithmetic average. To empirically evaluate the performance of the proposed algorithm, experiments are examined using 12 benchmark datasets, and corresponding results are compared with recent works. Results of statistical analysis indicate that the proposed algorithm is competitive in terms of the quality of solutions.