SMOTE.rar_SMOTE算法_matlab smote算法_matlab实现SMOTE_smote_smote算法matl

function [sample,sampleLabel]=undersampling(data,Label,ClassType,C,AttVector) % Implement under-sampling algorithm. % It changes the training data distribution by deleting some % lower-cost training examples until the appearances of different % training examples are proportional to their costs. Here a routine % similar to that used in [1] is employed, which removes redundant % examples at first and then removes borderline examples, the latter % can be detected using Tomek links [2]. % %Usage: % [sample,sampleLabel]=undersampling(data,Label,ClassType,C,attribute) % % sample: new training set after under-sampling to build cost-sensitive NN % format - row indexes attributes and column indexes % instances % sampleLabel: class labels for instances in new training set. % format - row vector % data: original training set. % format - row indexes attributes and column indexes instances % Label: class labels for instances in original training set % format - row vector % ClassType: class type % C: cost vector. the ith entry is the cost of misclassifying ith class % instance, without considering the concrete class the instance has % been wrongly assigned to. % AttVector: attribute vector,1 presents for the corresponding attribute % is nominal and 0 for numeric. % % Refer [1]: % M. Kubat and S. Matwin, ※Addressing the curse of imbalanced training % sets: one-sided selection,§ in Proceedings of the 14th International % Conference on Machine Learning, Nashville, TN, pp.179每186, 1997. % Refer [2]: % I. Tomek, ※Two modifications of CNN,§ IEEE Transactions on Systems, Man % and Cybernetics, vol.6, no.6, pp.769每772, 1976. %check parameters NumClass=length(ClassType); if(length(C)~=NumClass) error('class number does not consistent.') end if(size(data,2)~=size(Label)) error('instance numbers in data and Label do not consistent.') end if(size(data,1)~=length(AttVector)) error('attribute numbers in data and AttVector do not consistent.') end %compute class distribution ClassD=zeros(1,NumClass); for i=1:NumClass id=find(Label==ClassType(i)); ClassData{i}=data(:,id); ClassD(i)=length(id); end %compute new class distribution cn=C./ClassD; [tmp,baseClassID]=max(cn); newClassD=floor(C/C(baseClassID)*ClassD(baseClassID)); %ascending C [tmp,ascendC]=sort(C); %prepare for VDM used in the distance function attribute=VDM(data,Label,ClassType,AttVector); %sampling for i=ascendC if(newClassD(i)<ClassD(i)) D=[]; DL=[]; K=[]; % put instances of other classes into D for j=1:NumClass if(j~=i) D=[D ClassData{j}]; l=ones(1,ClassD(j)).*ClassType(j); DL=[DL l]; end end %break ClassData{i} into 2 parts n=floor(newClassD(i)/2); id=randperm(ClassD(i)); id1=id(1:n); id2=id(n+1:end); % put Ni*/2 class i instances into D D=[D ClassData{i}(:,id1)]; l=ones(1,n).*ClassType(i); DL=[DL l]; % put the remaining class i instances into K K=ClassData{i}(:,id2); K_flag=zeros(1,length(id2));% 0 unchanged,1 moved to D, -1 deleted diff=ClassD(i)-newClassD(i); NumDelIns=0; %check instances in K to delete redundant ones while(NumDelIns<diff & length(find(K_flag==0))>0) %randomly pick an unchecked instance to check id=find(K_flag==0); rn=round(rand(1,1)*(length(id)-1))+1; id=id(rn); instance=K(:,id); target=ClassType(i); % calculate distance which used for classification with 1-NN rule d=dist_nominal(instance,D,attribute,AttVector); % 1-NN [tmp,mind]=min(d); if(target==DL(mind))%delete K_flag(id)= -1; NumDelIns=NumDelIns+1; else% move to D K_flag(id)= 1; end end %if enough instances have been deleted, merge the remaining into D if(NumDelIns==diff) id=find(K_flag~= -1); D=[D K(:,id)]; l=ones(1,length(id))*ClassType(i); DL=[DL l]; K=[]; K_flag=[]; %if not else %merge unredundant instances into D id=find(K_flag==1); D=[D K(:,id)]; l=ones(1,length(id))*ClassType(i); DL=[DL l]; K=[]; K_flag=[]; %check the i-th class in D to delete borderline examples idClassi=find(DL==ClassType(i)); while(NumDelIns<diff & ~isempty(idClassi)) %randomly pick up an instance from the i-th class rn=round(rand(1,1)*(length(idClassi)-1))+1; id=idClassi(rn); X=D(:,id); target=ClassType(i); % calculate distances to identify Tomek links d=dist_nominal(X,D,attribute,AttVector); d(id)=inf; iid=find(isnan(DL)==1); d(iid)=nan; [tmp,NearX]=min(d); if(target~=DL(NearX)) Y=D(:,NearX); d=dist_nominal(Y,D,attribute,AttVector); d(NearX)=Inf; iid=find(isnan(DL)==1); d(iid)=nan; [tmp,NearY]=min(d); if(NearY==id)%delete borderline example DL(id)=NaN; NumDelIns=NumDelIns+1; end end idClassi=setdiff(idClassi,id); end%while id=find(isnan(DL)==0); D=D(:,id); DL=DL(id); % if it still needs to delete some instances, randomly delete % until requirement is meet if( isempty(idClassi) & NumDelIns<diff ) idClassi=find(DL==ClassType(i)); id=randperm(length(idClassi)); id=idClassi(id(1:diff-NumDelIns)); id=setdiff(1:length(DL),id); D=D(:,id); DL=DL(id); end end%if-elseif %update the i-th class after under-sampling id=find(DL==ClassType(i)); ClassData{i}=D(:,id); end%if end%for sample=D; sampleLabel=DL;