TA的每日心情 | 衰 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)" ?; d( c) M, C) p! |
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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! d, Y7 _1 N, |; D ?8 [适用场景:
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/ F( G( G* t. @7 d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。
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快速留一法KNN K& D/ _/ ^0 Q& e
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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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' K0 }: C0 N4 y* O6 y f( Y7 M主函数main.m
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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, R% w5 a: W$ f' r3 f: }, o
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?2 F2 ]- Z+ Q G4 ^数据集划分divide_dlbcl.m+ w3 `! D/ G6 b- k4 G% e& x! H
0 X; }) v9 R; b' R' j- 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. D& [% f# I/ W+ j. Z5 g. Z9 Q
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简单KNN* L5 @1 [$ e# h: S
8 B' S* _ Q p( }FSKNN1.m
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, ^" m/ i) y9 k! m$ |1 \- 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);
- end
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KNN1.m# h7 ~1 _0 g8 N0 }/ x
; A: O8 s0 C& m8 W# P4 F, o# I- 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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preKNN.m
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- 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
- end4 O. {1 H# L; C0 U) `. r4 U
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" y# }6 Y2 c1 O( g5 D- 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);
- end7 r! Z% z3 U# ]( B; }
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KNN2.m5 F! f8 ] K8 B
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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' C: M2 y2 ]% k7 g* c' E# X
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结果9 ^7 I) o z# [7 ^! i8 k
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6 Y2 U) c/ u" v- n可以看到FSKNN2+preKNN的时间比FSKNN1要少很多。7 U. t. n; P" p% b# u% e3 V
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