Semi-supervised method for gene expression data classification with Gaussian fields and harmonic functions

In real world applications, there are great many of DNA expressed microarray data, many supervised classification algorithms such as decision tree, KNN and SVM in the machine learning field have been introduced for microarray data classification. However, in real worlds, the labeled examples, especially gene expression data examples are often very difficult and expensive to obtain. The traditional supervised methods can not work well when lack of training examples. So in this paper, we propose to use the semi-supervised learning algorithms which learning with both labeled and unlabeled data to do classification for microarray data. We perform experiments on four public microarray data sets and the results showed the semi-supervised method holds a much higher classification accuracy than the supervised methods and is much more stable when the labeled examples are very few.