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2019-11-19 15:32 |
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签到天数: 1 天 [LV.1]初来乍到
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留一法交叉验证（LOOCV）+ c+ f3 ^8 ~6 B$ Y; b
留一法即Leave-One-Out Cross Validation。这种方法比较简单易懂，就是把一个大的数据集分为k个小数据集，其中k-1个作为训练集，剩下的一个作为测试集，然后选择下一个作为测试集，剩下的k-1个作为训练集，以此类推。其主要目的是为了防止过拟合，评估模型的泛化能力。计算时间较长。
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适用场景：
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% ?4 C3 q/ T- f; x/ E5 Y _数据集少，如果像正常一样划分训练集和验证集进行训练，那么可以用于训练的数据本来就少，还被划分出去一部分，这样可以用来训练的数据就更少了。loocv可以充分的利用数据。9 i2 x% X) H" i* n( y
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快速留一法KNN
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5 ^0 I% u5 \( w# B因为LOOCV需要划分N次，产生N批数据，所以在一轮训练中，要训练出N个模型，这样训练时间就大大增加。为了解决这样的问题，根据留一法的特性，我们可以提前计算出不同样本之间的距离（或者距离的中间值），存储起来。使用LOOCV时直接从索引中取出即可。下面的代码以特征选择为Demo，验证快速KNN留一法。
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其中FSKNN1是普通KNN，FSKNN2是快速KNN
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3 a# q! b# ?! n% d主函数main.m! T8 \4 U* A7 {/ o
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- clc
- [train_F,train_L,test_F,test_L] = divide_dlbcl();
- dim = size(train_F,2);
- individual = rand(1,dim);
- global choice
- choice = 0.5;
- global knnIndex
- [knnIndex] = preKNN(individual,train_F);
- for i = 1:100
- [error,fs] = FSKNN1(individual,train_F,train_L);
- [error2,fs2] = FSKNN2(individual,train_F,train_L);
- end
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数据集划分divide_dlbcl.m
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0 N' [5 b( n7 o, Z. g% o- function [train_F,train_L,test_F,test_L] = divide_dlbcl()
- load DLBCL.mat;
- dataMat=ins;
- len=size(dataMat,1);
- %归一化
- maxV = max(dataMat);
- minV = min(dataMat);
- range = maxV-minV;
- newdataMat = (dataMat-repmat(minV,[len,1]))./(repmat(range,[len,1]));
- Indices = crossvalind('Kfold', length(lab), 10);
- site = find(Indices==1|Indices==2|Indices==3);
- test_F = newdataMat(site,：);
- test_L = lab(site);
- site2 = find(Indices~=1&Indices~=2&Indices~=3);
- train_F = newdataMat(site2,：);
- train_L =lab(site2);
- end/ F: [9 Y8 o! M0 T8 V0 W6 L
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简单KNN& ], h5 w+ w! s! @
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FSKNN1.m, e% v+ Y5 E, F' q$ o
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- function [error,fs] = FSKNN1(x,train_F,train_L)
- global choice
- inmodel = x>choice;%%%%%设定恰当的阈值选择特征
- k=1;
- train_f=train_F(:,inmodel);
- train_length = size(train_F,1);
- flag = logical(ones(train_length,1));
- error=0;
- for j=1:train_length
- flag(j) = 0;
- CtrainF = train_f(flag,：);
- CtrainL = train_L(flag);
- CtestF = train_f(~flag,：);
- CtestL = train_L(~flag);
- classifyresult= KNN1(CtestF,CtrainF,CtrainL,k);
- if (CtestL~=classifyresult)
- error=error+1;
- end
- flag(j) = 1;
- end
- error=error/train_length;
- fs = sum(inmodel);
- end7 _0 @7 P1 y+ X1 c9 M f" Y
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KNN1.m* `- ]; G3 L6 U2 O1 P
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- function relustLabel = KNN1(inx,data,labels,k)
- %%
- % inx 为 输入测试数据，data为样本数据，labels为样本标签 k值自定1~3
- %%
- [datarow , datacol] = size(data);
- diffMat = repmat(inx,[datarow,1]) - data ;
- distanceMat = sqrt(sum(diffMat.^2,2));
- [B , IX] = sort(distanceMat,'ascend');
- len = min(k,length(B));
- relustLabel = mode(labels(IX(1:len)));
- end
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# |+ o7 d4 z7 L# j3 [快速KNN: C9 z& I- [5 a" G' A* K% x+ p
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preKNN.m& {, \9 k7 R5 i* W C
5 J7 N n' L; `7 e) J" h- function [knnIndex] = preKNN(x,train_F)
- inmodel = x > 0;
- train_f=train_F(:,inmodel);
- train_length = size(train_F,1);
- flag = logical(ones(train_length,1));
- knnIndex = cell(train_length,1);
- for j=1:train_length
- flag(j) = 0;
- CtrainF = train_f(flag,：);
- CtestF = train_f(~flag,：);
- [datarow , ~] = size(CtrainF);
- diffMat = repmat(CtestF,[datarow,1]) - CtrainF ;
- diffMat = diffMat.^2;
- knnIndex{j,1} = diffMat;
- flag(j) = 1;
- end
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FSKNN2.m P5 C& s$ X6 U
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- function [error,fs] = FSKNN2(x,train_F,train_L)
- global choice
- inmodel = x>choice;%%%%%设定恰当的阈值选择特征
- global knnIndex
- k=1;
- train_length = size(train_F,1);
- flag = logical(ones(train_length,1));
- error=0;
- for j=1:train_length
- flag(j) = 0;
- CtrainL = train_L(flag);
- CtestL = train_L(~flag);
- classifyresult= KNN2(CtrainL,k,knnIndex{j}(:,inmodel));
- if(CtestL~=classifyresult)
- error=error+1;
- end
- flag(j) = 1;
- end
- error=error/train_length;
- fs = sum(inmodel);
- end
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KNN2.m2 R: R- u$ n N' r1 m3 K( X
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- function relustLabel = KNN2(labels,k,diffMat)
- distanceMat = sqrt(sum(diffMat,2));
- [B , IX] = sort(distanceMat,'ascend');
- len = min(k,length(B));
- relustLabel = mode(labels(IX(1:len)));
- end
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结果
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可以看到FSKNN2+preKNN的时间比FSKNN1要少很多。7 M/ T' g8 g+ l# L7 K" p+ P
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发表于 2021-7-12 14:14
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