基于聚类和分类的基因表达数据快速约简算法的判定精度
Verdict Accuracy of Quick Reduct Algorithm using Clustering and Classification Techniques for Gene Expression Data
摘要：
在大多数基因表达数据中，与实验中涉及的大量基因相比，训练样本的数量非常少。然而，在大量的基因中，只有一小部分是有效的执行某种任务。此外，在开发基于基因表达的诊断工具以提供可靠和可理解的结果时，需要基因的小子集。利用基因选择结果，只需分析标记基因，就可以大大降低生物学实验和决策的成本。基因表达数据在功能基因组学中的一个重要应用是根据基因表达谱对样本进行分类。特征选择（FS）是试图选择更多信息特征的过程。它是知识发现的重要步骤之一。传统的监督FS方法使用评估函数或度量来评估各种特征子集，以仅选择与所考虑的数据的决策类相关的那些特征。研究了一种基于粗糙集理论的特征选择方法。进一步的K-Means，模糊C-均值（FCM）算法已经实现了减少的特征集，而不考虑类标签。然后将得到的结果与原始类别标签进行比较。反向传播网络（BPN）也被用于分类。然后通过混淆矩阵分析了K-Means、FCM和BPN算法的性能。结果发现，BPN表现相对较好。
英文摘要：
In most gene expression data, the number of training samples is very small compared to the large number of genes involved in the experiments. However, among the large amount of genes, only a small fraction is effective for performing a certain task. Furthermore, a small subset of genes is desirable in developing gene expression based diagnostic tools for delivering reliable and understandable results. With the gene selection results, the cost of biological experiment and decision can be greatly reduced by analyzing only the marker genes. An important application of gene expression data in functional genomics is to classify samples according to their gene expression profiles. Feature selection (FS) is a process which attempts to select more informative features. It is one of the important steps in knowledge discovery. Conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. This paper studies a feature selection method based on rough set theory. Further K-Means, Fuzzy C-Means (FCM) algorithm have implemented for the reduced feature set without considering class labels. Then the obtained results are compared with the original class labels. Back Propagation Network (BPN) has also been used for classification. Then the performance of K-Means, FCM, and BPN are analyzed through the confusion matrix. It is found that the BPN is performing well comparatively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
