Arrange the steps executed in the k$-$nearest neighbor classification algorithm.

$1)$ Determine the class from the k$-$nearest neighbor by taking majority vote of class labels among the k$-$nearest neighbors.

$2)$ Compute distance between two points. $($Example$,$ Euclidean distance$).$

$3)$ Identify the number of k$-$nearest neighbors to use.

$4)$ Rescale the feature using standardization and normalization.