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首页> 外文期刊>Biocybernetics and biomedical engineering >A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification
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A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification

机译：基于集成特征选择和改进的判别独立成分分析的特征提取新方法

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摘要

Microarray data play critical role in cancer classification. However, with respect to the samples scarcity compared to intrinsic high dimensionality, most approaches fail to classify small subset of genes. Feature selection techniques can reduce the dimension of the problem, which can reduce computational cost of the microarray data classification. However, previous studies have shown that feature extraction methods can also be useful in improving the performance of data classification. In this paper, we propose an ensemble schema for cancer diagnosis and classification that has three stages. At first, a hybrid filter based feature selection method using modified Bayesian logistic regression (BLogReg), Ttest and Fisher ratio is applied for selecting genes. In the second stage, selected genes are mapped via the proposed PSO-dICA method which is a modification of dICA. Finally, mapped features are classified using SVM classifier. To demonstrate the effectiveness of the proposed method, some traditional microarray data including Colon, Lung cancer, DLBCL, SRBCT, Leukemia-ALL and Prostate Tumor datasets are used. Experimental results show the efficiency and effectiveness of the proposed method. (C) 2016 Nalecz Institute of Biocybemetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier Sp. z o.o. All rights reserved.
机译：微阵列数据在癌症分类中起关键作用。但是，相对于与固有高维数相比的样本稀缺性，大多数方法无法对基因的一小部分进行分类。特征选择技术可以减小问题的范围，从而可以减少微阵列数据分类的计算成本。但是，先前的研究表明，特征提取方法也可以用于改善数据分类的性能。在本文中，我们提出了一个包含三个阶段的癌症诊断和分类的整体方案。首先，使用基于贝叶斯逻辑回归的改进的贝叶斯逻辑回归（BLogReg），Ttest和Fisher比率的特征选择方法来选择基因。在第二阶段，通过拟议的PSO-dICA方法对选定的基因进行定位，该方法是dICA的改进。最后，使用SVM分类器对映射的要素进行分类。为了证明该方法的有效性，使用了一些传统的微阵列数据，包括结肠癌，肺癌，DLBCL，SRBCT，白血病ALL和前列腺肿瘤数据集。实验结果表明了该方法的有效性和有效性。 （C）2016波兰科学院Nalecz生物仿生和生物医学工程研究所。由Elsevier Sp。发行。动物园。版权所有。

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