Azzera filtri
Azzera filtri

How to use fitcknn for multiple classes?

1 visualizzazione (ultimi 30 giorni)
Muhammad Kashif
Muhammad Kashif il 27 Set 2018
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
  2 Commenti
fatin suhana mohd khidzir
fatin suhana mohd khidzir il 19 Apr 2019
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 il 6 Mar 2020
hai fatin and kashif, can you send me the code of emotion recognition to my email syamizalloi@gmail.com.thank you

Accedi per commentare.

Risposte (0)

Categorie

Scopri di più su Statistics and Machine Learning Toolbox in Help Center e File Exchange

Community Treasure Hunt

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

Start Hunting!

Translated by