<Ul> <Li> rank the n l (\ displaystyle n_ (l)) features according to their mutual information with the class labels; </Li> <Li> for different values of K and m l ∈ (1,..., n l) (\ displaystyle m_ (l) \ in \ (1, \ ldots, n_ (l) \)), compute the classification error rate of a K - nearest neighbor (K - NN) classifier using only the m l (\ displaystyle m_ (l)) most informative features on a validation set; </Li> <Li> the value of m l (\ displaystyle m_ (l)) with which the classifier has reached the lowest error rate determines the number of features to retain . </Li> </Ul> <Li> rank the n l (\ displaystyle n_ (l)) features according to their mutual information with the class labels; </Li> <Li> for different values of K and m l ∈ (1,..., n l) (\ displaystyle m_ (l) \ in \ (1, \ ldots, n_ (l) \)), compute the classification error rate of a K - nearest neighbor (K - NN) classifier using only the m l (\ displaystyle m_ (l)) most informative features on a validation set; </Li> <Li> the value of m l (\ displaystyle m_ (l)) with which the classifier has reached the lowest error rate determines the number of features to retain . </Li>

Which of the following is an application of nn