feature selection

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Austin Richardson edited this page Jan 22, 2015 · 1 revision

I have decided to implement three types of feature selection:

Analysis of Variance (single feature).
Decision Tree + ANOVA (1+ features).
Decision Tree + ANOVA culled by feature location (1+ features, considers homology).

Class labels are determined by user but can be automatically assigned to either (a) taxonomic nomenclature or (b) label-free clustering.

All will be coupled with cross-validation in order to estimate variance and generate feature importance plots.

I need to implement these as IPython notebooks first in order to benchmark them.