How to use fitcknn for multiple classes?

조회 수: 1 (최근 30일)
Muhammad Kashif
Muhammad Kashif 2018년 9월 27일
댓글: Mohd Syamizal Mohd Isa 2020년 3월 6일
I am working on facial expression recognition. i made a dataset contain features & classes of 213 images.
  • Step1: Each row of my dataset represents the features of 1 image. so for 213 images 213 rows
  • Step2: the last column represents classes like; 1,2,3,4,5,6,7 i used fitcsvm it gives great results but now i want to use knn.
QUESTIONS
  1. How to use fitcknn or any knn classifier to classify
  2. along with cross-validation
  3. and find accuracy precision and recall
  4. help me with this code
clc;
close all;
data = load(fullfile('.', 'Features', 'jaffe_features.txt'));
% features_train = data(1:128,:);
% features_test = data(128:end,:);
nRows = size(data,1);
randRows = randperm(nRows); % generate random ordering of row indices
features = data(randRows(1:end),:);
labels1 = data(:,end);
[labels] = labels1;
Mdl = fitcknn(features,labels,'NumNeighbors',5,...
'ClassNames',{'1','2','3','4','5','6','7'},'Distance','euclidean', 'Standardize',1);
loss = resubLoss(Mdl);
CVMdl = crossval(Mdl);
classError = kfoldLoss(CVMdl);
label = predict(Mdl,features);
% plot confusion(features_test,idx)
% oofLabel = kfoldPredict(CVMdl);
% ConfMat = confusionmat(labels_test,label);
accuracy=confusionmatStats_2(labels_test,label);
% [m,n]=size(label);
%
% count=0;
% for i=1:m
% if(strcmp(labels_test(i),label(i)))
% count=count+1;
% end
% end
% Regards Regards
  댓글 수: 2
fatin suhana mohd khidzir
fatin suhana mohd khidzir 2019년 4월 19일
hai..i am doing the same knn and svm classifier as yours for facial expression recognition. can you teach me how to classify the 7 facial expression and label it by using knn and svm? can i have your email to learn futher from you? thank you
Mohd Syamizal Mohd Isa
Mohd Syamizal Mohd Isa 2020년 3월 6일
hai fatin and kashif, can you send me the code of emotion recognition to my email syamizalloi@gmail.com.thank you

댓글을 달려면 로그인하십시오.

답변 (0개)

카테고리

Help CenterFile Exchange에서 Statistics and Machine Learning Toolbox에 대해 자세히 알아보기

Community Treasure Hunt

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

Start Hunting!

Translated by