Machine Learning
Class prediction and discovery using gene expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Random Forests for multiclass classification: Random MultiNomial Logit
Expert Systems with Applications: An International Journal
IEEE Transactions on Information Technology in Biomedicine
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
By extracting significant samples (which we refer to as support vector samples as they are located only on support vectors), we can identify principal genes and then use these genes to classify cancers either by support vector machines (SVM) or back-propagation neural networking (BPNN). We call this approach the support vector sampling technique (SVST). No matter the number of genes selected, our SVST method shows a significant improvement of classification performance. Our SVST method has averages 2-3% better performance when applied to leukemia and 6-7% better performance when applied to prostate cancer.