期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2017
卷号：9
期号：3
页码：217
出版社：International Center for Scientific Research and Studies
摘要：A general problem faced in classification is a small number of samples compared with the large number of genes. In this case, the feature selection (FS) process becomes a challenging task to improve the classification performance by reducing the dimensionality. Particle swarm optimisation (PSO) is a powerful method for solving FS problems. The main ingredient of the binary PSO is its transfer function that allows mapping a continuous space to a discrete space. A sigmoid function was used to update positions in BPSO. However, due to its way of updating positions, this function is not very effective to dodge local minima and speed up the convergence. Thus, this paper suggests an enhanced BPSO, through the FS approach. The study employs a V type transfer function and a special method for updating positions. In addition, a hybrid FS method that integrates the information gain (IG) as a filter approach with the wrapper approach (EBPSO) is proposed. In a hybrid model, feature subsets are ranked based on their significance in a decreasing order. The EBPSO is applied to these subsets and then FS is performed. The proposed algorithm is tested on six microarray datasets and the results verify its superiority. Also, the results of the proposed hybrid FS model, supports its effectiveness because the model produced a small feature subset that showed high classification performance.
关键词：Feature Selection; Gene Expression Data; Hybrid Features Selection Particle Swarm Optimisation; Transfer Function.
