On the Stability of Feature Selection in Multiomics Data
| Autor: | Luca Pestarino, Giovanni Fiorito, Sergio Decherchi, Andrea Cavalli, Paolo Vineis, Silvia Polidoro |
|---|---|
| Přispěvatelé: | Pestarino, Luca, Fiorito, Giovanni, Polidoro, Silvia, Vineis, Paolo, Cavalli, Andrea, Decherchi, Sergio |
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
business.industry Computer science Feature extraction Stability (learning theory) Feature selection Machine learning computer.software_genre Data set Feature (computer vision) Redundancy (engineering) Artificial intelligence Cluster analysis business Machine learning feature selection clustering stability multiomics computer |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn52387.2021.9533806 |
| Popis: | Feature selection is a prominent activity when dealing with classification/regression problems in biological and omics data. Despite the effort devoted to this issue theoretically, feature selection stability within and across methods is often overlooked in practice. This is a compelling issue because a unique or at least stable answer is needed in clinical scenarios. Here, we analyse in detail a multiomics small sample data set, the Oxford Street II data set, and discuss how existing methods perform in terms of the usual metrics but also in terms of intra and inter feature selection stability. To mitigate the observed instability, we propose a simple unsupervised feature prefiltering, achieving promising results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|