Try this:
predClass = categorical(["N";"N";"A";"N";"N";"N";"A"]);
k = 2;
% find where there is an 'N' immediately preceded by an 'A' or a 'V'
bad_idx = 1 + find(predClass(2:end) == 'N' & (predClass(1:end-1) == 'A' | predClass(1:end-1) == 'V'));
% count those
k_add = numel(bad_idx);
% find all the 'N's in predClass
idxN = find(predClass == 'N');
% going to search for k+k_add+1 'N's because up to k_add will be removed
% for being in bad_idx (preceded by 'A' or 'V') and 1 will be removed
% for being itself
% limit the search value to be <= 1 less than the total number of 'N's in
% predClass (1 less to exclude each 'N' itself from its set of neighbors)
% and >= 0 (in case there are 0 'N's in predClass)
kk = max(0,min(k+k_add,numel(idxN)-1));
% perform mink search on the distance between the location of each 'N'
% and the location of all 'N's
[~,idx] = mink(abs(idxN-idxN.'),kk+1,1);
% remove the first row of idx, corresponding to distances of 0
idx(1,:) = [];
% convert idx from locations in idxN to locations in predClass
idx = idxN(idx);
% mark the "bad" 'N's as 0 in idx
idx(ismember(idx,bad_idx)) = 0;
% make each column an element of a cell array because we're about to remove
% a varying number of elements from each column (can't do that with a
% matrix and have it remain a matrix - each column might be of different
% length)
C = num2cell(idx,1);
% keep only the non-0s
C = cellfun(@(x)x(x~=0),C,'UniformOutput',false);
% keep only up to the 1st k elements in each column
C = cellfun(@(x)x(1:min(k,end)),C,'UniformOutput',false);
% make the result into a matrix, if possible
try
result = [C{:}]
catch
result = C
end
