Gene selection is one of the important issues for cancer classification based on gene expression profiles. Filter and wrapper approaches are widely used for gene selection, where the former is hard to measure the relationship between genes and the latter requires lots of computation. We present a novel method, called gene boosting, to select relevant gene subsets by integrating filter and wrapper approaches. It repeatedly selects a set of top-ranked informative genes by a filtering algorithm with respect to a temporal training dataset constructed according to the classification result for the original training dataset. Empirical results on three microarray benchmark datasets have shown that the proposed method is effective and efficient in finding a relevant and concise gene subset. It achieved competitive performance with fewer genes in a reasonable time, as well as led to the identification of some genes frequently getting selected.
Bibliographical noteFunding Information:
This work was supported by MKE, Korea under ITRC IITA-2009-(C1090-0902-0046) and KOSEF, Korea under (R01-2008-000-20801-0).
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence