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.
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