c4.5
9 views (last 30 days)
Show older comments
hi can anyone tell how to implement c4.5 algorithm plz for fisheris data ,is there any code for it
0 Comments
Answers (4)
Ilya
on 17 Apr 2012
Statistics Toolbox provides a decision tree implementation based on the book Classification and Regression Trees by Breiman et al (CART). If you have MATLAB 11a or later, do 'doc ClassificationTree' and 'doc RegressionTree'. If you have an older version, do 'doc classregtree'.
The CART algorithm is different from C4.5.
3 Comments
Muhammad Aasem
on 25 May 2012
I found the following source code for C4.5 algorithm. I hope it works for you:
function test_targets = C4_5(train_patterns, train_targets, test_patterns, inc_node)
% Classify using Quinlan's C4.5 algorithm
% Inputs:
% training_patterns - Train patterns
% training_targets - Train targets
% test_patterns - Test patterns
% inc_node - Percentage of incorrectly assigned samples at a node
%
% Outputs
% test_targets - Predicted targets
%NOTE: In this implementation it is assumed that a pattern 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_patterns);
inc_node = inc_node*M/100;
Nu = 10;
%Find which of the input patterns are discrete, and discretisize the corresponding
%dimension on the test patterns
discrete_dim = zeros(1,Ni);
for i = 1:Ni,
Ub = unique(train_patterns(i,:));
Nb = length(Ub);
if (Nb <= Nu),
%This is a discrete pattern
discrete_dim(i) = Nb;
dist = abs(ones(Nb ,1)*test_patterns(i,:) - Ub'*ones(1, size(test_patterns,2)));
[m, in] = min(dist);
test_patterns(i,:) = Ub(in);
end
end
%Build the tree recursively
disp('Building tree')
tree = make_tree(train_patterns, train_targets, inc_node, discrete_dim, max(discrete_dim), 0);
%Classify test samples
disp('Classify test samples using the tree')
test_targets = use_tree(test_patterns, 1:size(test_patterns,2), tree, discrete_dim, unique(train_targets));
%END
function targets = use_tree(patterns, indices, tree, discrete_dim, Uc)
%Classify recursively using a tree
targets = zeros(1, size(patterns,2));
if (tree.dim == 0)
%Reached the end of the tree
targets(indices) = tree.child;
return
end
%This is not the last level of the tree, so:
%First, find the dimension we are to work on
dim = tree.dim;
dims= 1:size(patterns,1);
%And classify according to it
if (discrete_dim(dim) == 0),
%Continuous pattern
in = indices(find(patterns(dim, indices) <= tree.split_loc));
targets = targets + use_tree(patterns(dims, :), in, tree.child(1), discrete_dim(dims), Uc);
in = indices(find(patterns(dim, indices) > tree.split_loc));
targets = targets + use_tree(patterns(dims, :), in, tree.child(2), discrete_dim(dims), Uc);
else
%Discrete pattern
Uf = unique(patterns(dim,:));
for i = 1:length(Uf),
if any(Uf(i) == tree.Nf) %Has this sort of data appeared before? If not, do nothing
in = indices(find(patterns(dim, indices) == Uf(i)));
targets = targets + use_tree(patterns(dims, :), in, tree.child(find(Uf(i)==tree.Nf)), discrete_dim(dims), Uc);
end
end
end
%END use_tree
function tree = make_tree(patterns, targets, inc_node, discrete_dim, maxNbin, base)
%Build a tree recursively
[Ni, L] = size(patterns);
Uc = unique(targets);
tree.dim = 0;
%tree.child(1:maxNbin) = zeros(1,maxNbin);
tree.split_loc = inf;
if isempty(patterns),
return
end
%When to stop: If the dimension is one or the number of examples is small
if ((inc_node > L) | (L == 1) | (length(Uc) == 1)),
H = hist(targets, length(Uc));
[m, largest] = max(H);
tree.Nf = [];
tree.split_loc = [];
tree.child = Uc(largest);
return
end
%Compute the node's I
for i = 1:length(Uc),
Pnode(i) = length(find(targets == Uc(i))) / L;
end
Inode = -sum(Pnode.*log(Pnode)/log(2));
%For each dimension, compute the gain ratio impurity
%This is done separately for discrete and continuous patterns
delta_Ib = zeros(1, Ni);
split_loc = ones(1, Ni)*inf;
for i = 1:Ni,
data = patterns(i,:);
Ud = unique(data);
Nbins = length(Ud);
if (discrete_dim(i)),
%This is a discrete pattern
P = zeros(length(Uc), Nbins);
for j = 1:length(Uc),
for k = 1:Nbins,
indices = find((targets == Uc(j)) & (patterns(i,:) == Ud(k)));
P(j,k) = length(indices);
end
end
Pk = sum(P);
P = P/L;
Pk = Pk/sum(Pk);
info = sum(-P.*log(eps+P)/log(2));
delta_Ib(i) = (Inode-sum(Pk.*info))/-sum(Pk.*log(eps+Pk)/log(2));
else
%This is a continuous pattern
P = zeros(length(Uc), 2);
%Sort the patterns
[sorted_data, indices] = sort(data);
sorted_targets = targets(indices);
%Calculate the information for each possible split
I = zeros(1, L-1);
for j = 1:L-1,
%for k =1:length(Uc),
% P(k,1) = sum(sorted_targets(1:j) == Uc(k));
% P(k,2) = sum(sorted_targets(j+1:end) == Uc(k));
%end
P(:, 1) = hist(sorted_targets(1:j) , Uc);
P(:, 2) = hist(sorted_targets(j+1:end) , Uc);
Ps = sum(P)/L;
P = P/L;
Pk = sum(P);
P1 = repmat(Pk, length(Uc), 1);
P1 = P1 + eps*(P1==0);
info = sum(-P.*log(eps+P./P1)/log(2));
I(j) = Inode - sum(info.*Ps);
end
[delta_Ib(i), s] = max(I);
split_loc(i) = sorted_data(s);
end
end
%Find the dimension minimizing delta_Ib
[m, dim] = max(delta_Ib);
dims = 1:Ni;
tree.dim = dim;
%Split along the 'dim' dimension
Nf = unique(patterns(dim,:));
Nbins = length(Nf);
tree.Nf = Nf;
tree.split_loc = split_loc(dim);
%If only one value remains for this pattern, one cannot split it.
if (Nbins == 1)
H = hist(targets, length(Uc));
[m, largest] = max(H);
tree.Nf = [];
tree.split_loc = [];
tree.child = Uc(largest);
return
end
if (discrete_dim(dim)),
%Discrete pattern
for i = 1:Nbins,
indices = find(patterns(dim, :) == Nf(i));
tree.child(i) = make_tree(patterns(dims, indices), targets(indices), inc_node, discrete_dim(dims), maxNbin, base);
end
else
%Continuous pattern
indices1 = find(patterns(dim,:) <= split_loc(dim));
indices2 = find(patterns(dim,:) > split_loc(dim));
if ~(isempty(indices1) | isempty(indices2))
tree.child(1) = make_tree(patterns(dims, indices1), targets(indices1), inc_node, discrete_dim(dims), maxNbin, base+1);
tree.child(2) = make_tree(patterns(dims, indices2), targets(indices2), inc_node, discrete_dim(dims), maxNbin, base+1);
else
H = hist(targets, length(Uc));
[m, largest] = max(H);
tree.child = Uc(largest);
tree.dim = 0;
end
end
3 Comments
Samir Darouich
on 11 Jan 2016
Muhammad Aasem is this matlab code for C4.5 algorithm available for all types of clasifications induction trees (I mean is it general)? or it's available for a concrete aplication? and I'de like you to give a brief explanation please if you can.
UMA RAMASAMY
on 21 May 2019
Edited: UMA RAMASAMY
on 21 May 2019
for muhammad aasem c4.5 coding ...how to give input and output for these function using iris dataset. can anyone help me?
Ilya
on 19 Apr 2012
I don't know if you can use classregtree for "c4.5 classification". If you are looking for a decision tree implementation, you can use classregtree. If you are looking specifically for the C4.5 algorithm, obviously you cannot use classregtree. If you don't know enough to choose one algorithm over the other, perhaps you should use whatever is readily available, that is, classregtree.
I am not going to answer your 2nd question in this thread. I'll note however that the error message gives you a clue. classperf expects class indices, and you are giving it what exactly?
See Also
Categories
Find more on Get Started with Statistics and Machine Learning Toolbox in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!