K$-$NN classifier is likely to overfit the data due to curse of dimensionality $($availability of more features than needed$).$ Which of the following options would you consider for handling such a problem?

$($A$)$ Feature Omission

$($B$)$ Feature Selection and$/$or Feature Engineering

$($C$)$ increase K $($where K is the number of neighbors in a cluster$)$

$($D$)$ Decrease K $($where K is the number of neighbors in a cluster$)$

OD and B

OD and A

A and B

A and C