Yu H L, Gao S, Qin B, Zhao J
School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, China.
Genet Mol Res. 2012 May 15;11(2):1357-69. doi: 10.4238/2012.May.15.6.
Microarray technology is becoming a powerful tool for clinical diagnosis, as it has potential to discover gene expression patterns that are characteristic for a particular disease. To date, this possibility has received much attention in the context of cancer research, especially in tumor classification. However, most published articles have concentrated on the development of binary classification methods while neglected ubiquitous multiclass problems. Unfortunately, only a few multiclass classification approaches have had poor predictive accuracy. In an effort to improve classification accuracy, we developed a novel multiclass microarray data classification method. First, we applied a "one versus rest-support vector machine" to classify the samples. Then the classification confidence of each testing sample was evaluated according to its distribution in feature space and some with poor confidence were extracted. Next, a novel strategy, which we named as "class priority estimation method based on centroid distance", was used to make decisions about categories for those poor confidence samples. This approach was tested on seven benchmark multiclass microarray datasets, with encouraging results, demonstrating effectiveness and feasibility.
微阵列技术正成为临床诊断的有力工具，因为它有潜力发现特定疾病所特有的基因表达模式。迄今为止，这种可能性在癌症研究领域，尤其是肿瘤分类方面受到了广泛关注。然而，大多数已发表的文章都集中在二元分类方法的开发上，而忽略了普遍存在的多类问题。不幸的是，只有少数多类分类方法的预测准确率较低。为了提高分类准确率，我们开发了一种新颖的多类微阵列数据分类方法。首先，我们应用“一对其余支持向量机”对样本进行分类。然后根据每个测试样本在特征空间中的分布评估其分类置信度，并提取一些置信度较低的样本。接下来，我们使用一种新颖的策略，即“基于质心距离的类优先级估计方法”，对那些置信度较低的样本进行类别决策。该方法在七个基准多类微阵列数据集上进行了测试，结果令人鼓舞，证明了其有效性和可行性。
