An effective hybrid approach of gene selection and classification for microarray data based on clustering and particle swarm optimisation

Pub Date : 2015-08-01 DOI:10.1504/IJDMB.2015.071515
Fei Han, Shanxiu Yang, Jian Guan
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引用次数: 5

Abstract

In this paper, a hybrid approach based on clustering and Particle Swarm Optimisation (PSO) is proposed to perform gene selection and classification for microarray data. In the new method, firstly, genes are partitioned into a predetermined number of clusters by K-means method. Since the genes in each cluster have much redundancy, Max-Relevance Min-Redundancy (mRMR) strategy is used to reduce redundancy of the clustered genes. Then, PSO is used to perform further gene selection from the remaining clustered genes. Because of its better generalisation performance with much faster convergence rate than other learning algorithms for neural networks, Extreme Learning Machine (ELM) is chosen to evaluate candidate gene subsets selected by PSO and perform samples classification in this study. The proposed method selects less redundant genes as well as increases prediction accuracy and its efficiency and effectiveness are verified by extensive comparisons with other classical methods on three open microarray data.
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基于聚类和粒子群优化的基因选择与分类的有效混合方法
本文提出了一种基于聚类和粒子群优化(PSO)的混合方法对微阵列数据进行基因选择和分类。该方法首先利用K-means方法将基因划分为预定数量的聚类;由于每个聚类中的基因具有较大的冗余度,采用最大相关最小冗余度(mRMR)策略来降低聚类基因的冗余度。然后,利用粒子群算法从剩余的聚类基因中进行进一步的基因选择。由于极限学习机(Extreme learning Machine, ELM)具有比其他神经网络学习算法更好的泛化性能和更快的收敛速度,本研究选择极限学习机(Extreme learning Machine, ELM)对粒子群算法选择的候选基因子集进行评估并进行样本分类。通过与其他经典方法在三个开放芯片数据上的广泛比较,验证了该方法的效率和有效性。
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