用SVM做特征选择

function [feature,accuracy]=CFSPabs_SBFS(data_input) %% % 实现 CFSPabs 与SVM的顺序后向 浮动 混合特征选择 %% [m,n]=size(data_input); % [ gi ,giF ]=f_score( data_input ); rest_fea=1:1:n-1; select_fea=rest_fea; %% 首先使用 全部特征子集 训练SVM，得到一个平均正确率 tem_acc=[]; % feature={}; indices=crossvalind('Kfold',data_input(1:m,n),5); % 十折交叉验证 随机分包 for i=1:5 test=(indices==i); % 获得test集元素在数据集中对应的单元编号 train=~test; % train集元素的编号为非test元素的编号 train_data=data_input(train,:); % 从数据集中划分出train样本的数据 test_data=data_input(test,:); [pre_label,acc]=SVM_SFS(train_data,rest_fea); tem_acc=[tem_acc,acc(1,1)]; end max_acc=mean(tem_acc); % feature{1}=rest_fea; %% 尝试从剩余特征子集中去除一个特征，并计算相应的DFS的值 k=1; % while ~isempty(rest_fea) while length(rest_fea)-1>0 dfs=[]; k=k+1; d=size(rest_fea,2); for i=1:d data=[]; visit=zeros(1,d); %% visit(i)=1; rest=(visit==1); select=~rest; tem_fea=rest_fea(1,select); len=size(tem_fea,2); % tem_fea=select(1,rest); %% for j=1:len data(:,j)=data_input(:,tem_fea(j)); end data(:,end+1)=data_input(:,end); %加类标列 ms=CFSPabs_score(data); % 计算临时特征的DFS值 % dfs(i)=dfsi(k); % if dfsi(k)>tem1 % tem1=dfsi(k); % max=rest_fea(i); % end % select_fea=[select_fea,max]; % if rest_fea(j)==max dfs=[dfs,ms]; end [~,ind]=sort(dfs,'descend'); visit=zeros(1,d); visit(ind(1))=1; delete_fea=rest_fea(ind(1)); % 本次去除的特征编号 rest1=(visit==1); select1=~rest1; rest_fea=rest_fea(1,select1); %% 计算特征数减 1 的特征子集的正确率 tem_acc=[]; indices=crossvalind('Kfold',data_input(1:m,n),5); % 十折交叉验证 随机分包 for i=1:5 test=(indices==i); % 获得test集元素在数据集中对应的单元编号 train=~test; % train集元素的编号为非test元素的编号 train_data=data_input(train,:); % 从数据集中划分出train样本的数据 test_data=data_input(test,:); [pre_label,acc]=SVM_SFS(train_data,rest_fea); tem_acc=[tem_acc,acc(1,1)]; end meanAcc=mean(tem_acc); % accuracy=[accuracy,mean(tem_acc)]; if meanAcc>=max_acc % delete_fea=rest_fea(ind(1)); len1=length(select_fea); for i=1:len1 if select_fea(i)==delete_fea visit=zeros(1,len1); visit(i)=1; rest2=(visit==1); select2=~rest2; select_fea=select_fea(1,select2); break; end end end max_acc=meanAcc; end feature=select_fea; accuracy=max_acc; end