Small sample feature selection
| dc.contributor | Dougherty, Edward R. | |
| dc.creator | Sima, Chao | |
| dc.date.accessioned | 2007-09-17T19:33:11Z | |
| dc.date.accessioned | 2017-04-07T19:53:19Z | |
| dc.date.available | 2007-09-17T19:33:11Z | |
| dc.date.available | 2017-04-07T19:53:19Z | |
| dc.date.created | 2003-05 | |
| dc.date.issued | 2007-09-17 | |
| dc.description.abstract | High-throughput technologies for rapid measurement of vast numbers of biolog- ical variables offer the potential for highly discriminatory diagnosis and prognosis; however, high dimensionality together with small samples creates the need for fea- ture selection, while at the same time making feature-selection algorithms less reliable. Feature selection is required to avoid overfitting, and the combinatorial nature of the problem demands a suboptimal feature-selection algorithm. In this dissertation, we have found that feature selection is problematic in small- sample settings via three different approaches. First we examined the feature-ranking performance of several kinds of error estimators for different classification rules, by considering all feature subsets and using 2 measures of performance. The results show that their ranking is strongly affected by inaccurate error estimation. Secondly, since enumerating all feature subsets is computationally impossible in practice, a suboptimal feature-selection algorithm is often employed to find from a large set of potential features a small subset with which to classify the samples. If error estimation is required for a feature-selection algorithm, then the impact of error estimation can be greater than the choice of algorithm. Lastly, we took a regression approach by comparing the classification errors for the optimal feature sets and the errors for the feature sets found by feature-selection algorithms. Our study shows that it is unlikely that feature selection will yield a feature set whose error is close to that of the optimal feature set, and the inability to find a good feature set should not lead to the conclusion that good feature sets do not exist. | |
| dc.identifier.uri | http://hdl.handle.net/1969.1/5796 | |
| dc.language.iso | en_US | |
| dc.publisher | Texas A&M University | |
| dc.subject | feature selection | |
| dc.subject | classification | |
| dc.subject | microarray | |
| dc.subject | small sample | |
| dc.title | Small sample feature selection | |
| dc.type | Book | |
| dc.type | Thesis |