All-relevant feature selection in Machine Learning: methods and applications

In the talk I will present short overview of general approaches to feature selection and explain the need for the all-relevant feature selection. I will give short motivation for the need of the feature selection methods that take into account synergistic interactions. Then I will describe two algorithms that I have proposed, Boruta and MDFS. Finally I will show some examples of real life applications and discuss caveats and limitations of these methods.

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