function D = C4_5(train_features, train_targets, inc_node, region) % Classify using Quinlan's C4.5 algorithm % Inputs: % features - Train features % targets - Train targets % inc_node - Percentage of incorrectly assigned samples at a node % region - Decision region vector: [-x x -y y number_of_points] % % Outputs % D - Decision sufrace %NOTE: In this implementation it is assumed that a feature vector with fewer than 10 unique values (the parameter Nu) %is discrete, and will be treated as such. Other vectors will be treated as continuous [Ni, M] = size(train_features); inc_node = inc_node*M/100; Nu = 10; %For the decision region N = region(5); mx = ones(N,1) * linspace (region(1),region(2),N); my = linspace (region(3),region(4),N)' * ones(1,N); flatxy = [mx(:), my(:)]'; %Preprocessing %[f, t, UW, m] = PCA(train_features, train_targets, Ni, region); %train_features = UW * (train_features - m*ones(1,M));; %flatxy = UW * (flatxy - m*ones(1,N^2));; %Find which of the input features are discrete, and discretisize the corresponding %dimension on the decision region discrete_dim = zeros(1,Ni); for i = 1:Ni, Nb = length(unique(train_features(i,:))); if (Nb <= Nu), %This is a discrete feature discrete_dim(i) = Nb; [H, flatxy(i,:)] = high_histogram(flatxy(i,:), Nb); end end %Build the tree recursively disp( 'Building tree') tree = make_tree(train_features, train_targets, inc_node, discrete_dim, max(discrete_dim), 0); %Make the decision region according to the tree disp( 'Building decision surface using the tree') targets = use_tree(flatxy, 1:N^2, tree, discrete_dim, unique(train_targets)); D = reshape(targets,N,N); %END function targets = use_tree(features, indices, tree, discrete_dim, Uc) %Classify recursively using a tree targets...
