Department of Electronics and Communication Engineering, MCKV Institute of Engineering, 243, G.T. Road (N), Liluah, Howrah.
IEEE/ACM Trans Comput Biol Bioinform. 2011 May-Jun;8(3):659-71. doi: 10.1109/TCBB.2010.36.
The most important application of microarray in gene expression analysis is to classify the unknown tissue samples according to their gene expression levels with the help of known sample expression levels. In this paper, we present a nonparallel plane proximal classifier (NPPC) ensemble that ensures high classification accuracy of test samples in a computer-aided diagnosis (CAD) framework than that of a single NPPC model. For each data set only, a few genes are selected by using a mutual information criterion. Then a genetic algorithm-based simultaneous feature and model selection scheme is used to train a number of NPPC expert models in multiple subspaces by maximizing cross-validation accuracy. The members of the ensemble are selected by the performance of the trained models on a validation set. Besides the usual majority voting method, we have introduced minimum average proximity-based decision combiner for NPPC ensemble. The effectiveness of the NPPC ensemble and the proposed new approach of combining decisions for cancer diagnosis are studied and compared with support vector machine (SVM) classifier in a similar framework. Experimental results on cancer data sets show that the NPPC ensemble offers comparable testing accuracy to that of SVM ensemble with reduced training time on average.
微阵列在基因表达分析中的最重要应用是根据已知样本的表达水平，通过帮助未知组织样本的基因表达水平进行分类。在本文中，我们提出了一种非平行平面近端分类器（NPPC）集成，该集成在计算机辅助诊断（CAD）框架中比单个 NPPC 模型能够确保对测试样本的高分类准确性。对于每个数据集，仅使用互信息标准选择少数几个基因。然后，使用基于遗传算法的同时特征和模型选择方案，通过最大化交叉验证准确性，在多个子空间中训练多个 NPPC 专家模型。通过在验证集上对训练模型的性能进行选择，来选择集成成员。除了常用的多数投票方法外，我们还为 NPPC 集成引入了基于最小平均接近度的决策组合器。在类似的框架中，研究并比较了 NPPC 集成和提出的用于癌症诊断的新决策组合方法的有效性，与支持向量机（SVM）分类器进行比较。在癌症数据集上的实验结果表明，NPPC 集成在平均训练时间减少的情况下，可提供与 SVM 集成相当的测试准确性。
