Basic info abut the experiment:
- Binary classification of exons
- 10 fold cross validation
- 1200 features of exons are ranked by Fisher Score, Relief and Gini Index feature selection algorithms
- 1-nearest neighbor classifier is applied to the ranked list of features.
- The classifier is trained on 1 best feature, on 2 best features, on 3 best features, ...
There is no significant difference between Random Feature Selection and Automatic Feature Selection.
What are some potential reasons for no significant difference between Random Feature Selection and Automatic Feature Selection? Does that mean that all of 1200 features are strongly relevant?
Reference: G. H. John, R. Kohavi, and P. Pfleger. Irrelevant features and the subset selection problem. In Machine Learning: Proceedings of the Eleventh Inter- national Conference. Morgan Kaufmann, 1994.