论文标题
扩展粒子群优化（EPSO）用于高维生物医学数据的特征选择
Extended Particle Swarm Optimization (EPSO) for Feature Selection of High Dimensional Biomedical Data
论文作者
论文摘要
本文提出了一种新型的扩展粒子群优化模型（EPSO），该模型有可能增强PSO的搜索过程以进行优化问题。显然，基因表达谱是用于癌症类型的医学诊断的分子生物学中显着重要的测量因子。某些分类方法的基因表达谱的挑战在于每个样本记录的数千个特征。应用了修改的包装器特征选择模型，目的是通过分别用EPSO和PSO替换其随机性方法来应对基因分类挑战。 EPSO正在初始化人口的随机大小，并将其分为两组，以促进探索并减少停滞的可能性。在实验上，EPSO比PSO（平均95.72秒）选择最佳特征（平均62.14秒）所需的处理时间更少。此外，EPSO的准确性比PSO提供了更好的分类结果（从54％到100％开始）（从52％开始到96％）。
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%).
