Name
Abstract | ||
|---|---|---|
A major difficulty in bioinformatics is due to the size of the datasets, which contain frequently large numbers of variables.
In this study, we present a two-step procedure for feature selection. In a first “filtering” stage, a relatively small subset
of features is identified on the basis of several criteria. In the second stage, the importance of the selected variables
is evaluated based on the frequency of their participation in relevant patterns and low impact variables are eliminated. This
step is applied iteratively, until arriving to a Pareto-optimal “support set”, which balances the conflicting criteria of
simplicity and accuracy. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/s10479-006-0084-x | Annals OR |
Keywords | Field | DocType |
Feature selection,Genomics,Proteomics,Logical analysis of data,LAD,Patterns | Data mining,Feature selection,Proteomics,Logical analysis of data,Filter (signal processing),Genomics,Mathematics | Journal |
Volume | Issue | ISSN |
148 | 1 | 0254-5330 |
Citations | PageRank | References |
15 | 0.93 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriela Alexe | 1 | 197 | 12.75 |
| sorin alexe | 2 | 169 | 10.56 |
| Peter L. Hammer | 3 | 1996 | 288.93 |
| Béla Vizvári | 4 | 77 | 9.40 |