文档介绍：functionD=C4_5(train_features,train_targets,inc_node,region)
%ClassifyusingQuinlan'
%Inputs:
%features-,discrete_dim(dims),Uc);
in=indices(find(features(dim,indices)>));
targets=targets+use_tree(features(dims,:),in,(2),discrete_dim(dims),Uc);
else
%Discretefeature
Uf=unique(features(dim,:));fori=1:length(Uf),
in=indices(find(features(dim,indices)==Uf(i)));
targets=targets+use_tree(features(dims,:),in,(i),discrete_dim(dims),Uc);
end
end
%ENDuse_tree
functiontree=make_tree(features,targets,inc_node,discrete_dim,maxNbin,base)
%Buildatreerecursively
[Ni,L]
Uc
%(1:maxNbin)
=size(features);
=unique(targets);
=0;
zeros(1,maxNbin);
=inf;
ifisempty(features),
break
end
%Whentostop:Ifthedimensionisoneorthenumberofexamplesissmall
if((inc_node>L)|(L==1)|(length(Uc)==1)),
H=hist(targets,length(Uc));[m,largest]=max(H);
=Uc(largest);
break
end
%Computethenode'sI
fori=1:length(Uc),
Pnode(i)=length(find(targets==Uc(i)))/L;end
Inode=-sum(Pnode.*log(Pnode)/log(2));
%Foreachdimension,computethegainratioimpurity%Thisisdoneseparatelyfordiscreteandcontinuousfeatures
delta_Ib=zeros(1,Ni);
split_loc=ones(1,Ni)*inf;
fori=1:Ni,
data=features(i,:);
Nbins=length(unique(data));if(discrete_dim(i)),
%Thisisadiscretefeature
P=zeros(length(Uc),Nbins);
forj=1:length(Uc),