Abstract HTML Views: 388 PDF Downloads: 160 Total Views/Downloads: 548
Abstract HTML Views: 220 PDF Downloads: 114 Total Views/Downloads: 334
For the tumor gene expression profile data that aiming to high-dimension small samples, how to select the classification
feature of samples among thousands genes effectively is the difficult problems for analysis on tumor gene expression
profile. First to partition the data set into K average divisions, to use Lasso method performing feature selection
on each respectively, and then merge each selected division of subset together to perform feather selection again, and get
the final feature gene. This experiment adopts the Support Vector Machine (SVM) as classifier, to take the classification
performance of feature gene set by Leave One Out Cross-Validation (LOOCV) method as evaluation standard, improve
classification accuracy and with algorithm in good stability. Because of lowered dimensions in each time of calculation, it
solves the problem of overhead computational-expensive, and also solves the problem of “over-fitting” in a certain grade.
Thus it gets conclusion that the K-partitioning Lasso method shall be an effective method for tumor feature gene selection.