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Microarray Gene Feature Classification based on LS-SVM

机译：基于LS-SVM的微阵列基因特征分类

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DNA microarray has the characteristics of the higher dimension and redundancy, they bring into a series of the difficulties for the gene feature clasiffication. Pertaining to the two classical microarray datasets (cancer of colon set and leukemia set), firstly, the preprocess has been taken by the normalizing and the redundant data have been withdrawn; secondly, Principal Component Analysis method has been adopted to reduce the dimension of datasets and the information gene sets have been obtained; Finally, multiple classifiers have been utilized for the simulating tests, such as LS-SVM, SVM, BP, RBF, etc. They demonstrate that LS-SVM classifier has the higher accuracy for classification and show the approached method can make the correct judgment for classifying the feature of gene dataset, and provide the verifying reliance for clinical therapy further.
机译：DNA微阵列具有更高的维度和冗余的特点，它们为基因特征施用的一系列困难。与两个古典微阵列数据集（结肠集和白血病组癌癌）有关，首先，预处理是由归一化的，冗余数据被撤回;其次，已采用主成分分析方法来减少数据集的尺寸，并获得了信息基因集;最后，已经利用了多分类器来用于模拟测试，例如LS-SVM，SVM，BP，RBF等。它们证明LS-SVM分类器具有更高的分类精度，并显示接近的方法可以做出正确的判断分类基因数据集的特征，并提供临床治疗的验证依赖。

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