Department of Computer Sciences, Faculty of Mathematical Sciences, Tarbiat Modares University, P.O. Box 14115-134, Tehran, Iran.
Comput Math Methods Med. 2012;2012:320698. doi: 10.1155/2012/320698. Epub 2012 Jul 26.
We develop a detection model based on support vector machines (SVMs) and particle swarm optimization (PSO) for gene selection and tumor classification problems. The proposed model consists of two stages: first, the well-known minimum redundancy-maximum relevance (mRMR) method is applied to preselect genes that have the highest relevance with the target class and are maximally dissimilar to each other. Then, PSO is proposed to form a novel weighted SVM (WSVM) to classify samples. In this WSVM, PSO not only discards redundant genes, but also especially takes into account the degree of importance of each gene and assigns diverse weights to the different genes. We also use PSO to find appropriate kernel parameters since the choice of gene weights influences the optimal kernel parameters and vice versa. Experimental results show that the proposed mRMR-PSO-WSVM model achieves highest classification accuracy on two popular leukemia and colon gene expression datasets obtained from DNA microarrays. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.
我们开发了一种基于支持向量机（SVM）和粒子群优化（PSO）的检测模型，用于基因选择和肿瘤分类问题。所提出的模型由两个阶段组成：首先，应用著名的最小冗余最大相关性（mRMR）方法来预选择与目标类相关性最高且彼此差异最大的基因。然后，提出 PSO 来形成一种新的加权 SVM（WSVM）来对样本进行分类。在这个 WSVM 中，PSO 不仅剔除了冗余基因，而且特别考虑了每个基因的重要程度，并为不同的基因分配不同的权重。我们还使用 PSO 来找到合适的核参数，因为基因权重的选择会影响最优核参数，反之亦然。实验结果表明，在所提出的基于 mRMR-PSO-WSVM 的模型在两个来自 DNA 微阵列的流行白血病和结肠癌基因表达数据集上实现了最高的分类准确性。因此，与以前的报告结果相比，我们可以得出结论，我们提出的方法非常有前途。
