I need help about the imbalanced data

조회 수: 1 (최근 30일)
aslan kaya
aslan kaya 2021년 9월 21일
댓글: aslan kaya 2021년 9월 21일
i have a data set , but i couldn't manage to get a good solution . I will be apprecite if you help me to handle imbalanced data. Thanks a lot

채택된 답변

KSSV
KSSV 2021년 9월 21일
편집: KSSV 2021년 9월 21일
Read about readtable.
After reading you can access the rspective columns using T.T1, T.T2 etc... or T.(1), T.(2) etc.
T = readtable('https://in.mathworks.com/matlabcentral/answers/uploaded_files/744984/data.xlsx')
T = 500×9 table
T1 T2 T3 T4 T5 T6 T7 T8 RESULT ____ ____ ____ ____ ____ ____ ____ ____ ______ 12.5 6.25 1.05 11 3 1.3 1.75 1.6 3 2.7 2.7 2.2 3.35 1.75 2.8 1.55 1.8 2 2.5 2.9 2.2 3.2 1.75 3 1.65 1.7 3 2.5 2.9 2.2 3.05 1.85 2.8 1.45 1.95 3 2.7 3 2 3.5 1.8 2.8 1.65 1.7 2 2.5 3.1 2.1 3.05 2 2.8 1.3 2.3 2 9 4 1.2 7.5 2.35 1.6 1.85 1.5 1 3.4 3.3 1.65 3.75 2.2 2.15 1.5 1.85 2 3.2 3 1.8 3.75 1.8 2.55 1.7 1.65 3 8 4.3 1.2 7 2.5 1.5 1.6 1.75 3 5.5 3.6 1.35 5.25 2.3 1.75 1.7 1.65 3 2.5 3.1 2.1 3.2 1.95 2.7 1.55 1.8 3 2.5 2.7 2.3 3.2 1.7 3.05 1.65 1.7 3 5.5 4.6 1.25 5 2.6 1.6 1.55 1.8 3 6.25 3.75 1.3 5.25 2.4 1.65 1.55 1.8 3 4 3.6 1.5 4.25 2.4 1.9 1.35 2.15 3
  댓글 수: 5
KSSV
KSSV 2021년 9월 21일
Neural network for what?
aslan kaya
aslan kaya 2021년 9월 21일
clear all;
close all;
clc
data=xlsread('data.xlsx');
input=data(:,1:8);
output=data(:,end);
x=input';
t=output';
trainFcn='trainlm';
hiddenLayerSize=[10 8 3];
net=feedforwardnet(hiddenLayerSize, trainFcn);
net.layers(1).transferFcn='tansig';
net.layers(2).transferFcn='tansig';
net.layers(3).transferFcn='tansig';
net.input.processFcns={'removeconstantrows', 'mapminmax'};
net.output.processFcns={'removeconstantrows', 'mapminmax'};
net.divideFcn='dividerand';
net.divideMode='sample';
net.divideParam.trainRatio=70/100;
net.divideParam.valRatio=20/100;
net.divideParam.testRatio=10/100;
net.trainParam.show=25;
net.trainParam.lr=0.001;
net.trainParam.epochs=100;
net.trainParam.goal=0;
net.trainParam.max_fail=50;
net.trainParam.mc=0.9;
net.trainParam.min_grad=0;
net.performFcn='mse';
net.plotFcns={'plotform', 'plottrainstate','ploterrhist','plotregression', 'plotfit'};
[net,tr]=train(net,x,t);
y=net(x);
e=gsubtract(t,y);
performance=perform(net,t,y);
trainTargets=t.*tr.trainMask(1);
valTargets=t.*tr.valnMask(1);
testTargets=t.*tr.testMask(1);
trainPerformance=perform(net,trainTargets,y);
valPerformance=perform(net,valTargets,y);
testPerformance=perform(net,testTargets,y);
a=trainFcn;
b=hiddenLayerSize;
f1=net.layers(1).transferFcn;
f2=net.layers(2).transferFcn;
f3=net.layers(3).transferFcn;
f=trainPerformance;
g=valPerformance;
k=testPerformance;
result=[a,',',num2str(b),',',f1,',',f2,',',f3,',', num2str(f),',',num2str(g),',',num2str(k)];
disp(result)

댓글을 달려면 로그인하십시오.

추가 답변 (0개)

카테고리

Help CenterFile Exchange에서 Programming에 대해 자세히 알아보기

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by