Guilt-By-Association feature selection applied to simulated proteomic data.
We propose a new feature selection algorithm, Guilt-By-Association (GBA), which uses hierarchical clustering based on feature correlations to eliminate redundant features. GBA can be used in conjunction with other algorithms to produce a feature selection routine that explicitly considers both the similarities between features and their individual discriminatory powers. In this preliminary study, a simple form of GBA was investigated on simulated proteomic data.
Shin
Hyunjin
H
Department of Electrical and Computer Engineering, The University of Texas at Austin, TX, USA.
Sheu
Bryan
B
Markey
Mia K
MK
D000465:Algorithms; D015415:Biomarkers / Q000032:analysis; D013058:Mass Spectrometry; D020543:Proteome
D015415:Biomarkers; D020543:Proteome