How calculate the class membership of each training sample using Fuzzy-kNN

I want to use the FKNN to classify my data, but I can't calculate the class of membership of each training sample. I tried to write a small code, but it does not work, I want to know why that. attached my code, input data

Answer 1

[Deep]

Hi Merlin,

As per my understanding, you are implementing the Fuzzy K-Nearest Neighbours (FKNN) algorithm to classify your data. There seems to be an issue with calculating the class membership of each test sample, particularly in handling distance calculations and membership degree computations.

In the provided code, the use of “cumsum” for membership calculations do not align with FKNN requirements. Here is a concise snippet that demonstrates how to compute the fuzzy membership for each class:

num_classes = 5;
y_est = zeros(size(x_test, 1), num_classes);

% Calculate fuzzy membership
for i = 1:size(x_test, 1)
    membership = zeros(1, num_classes);
    for j = 1:k
        class_label = y_train(idx(i, j));
        weight = 1 / (dists(i, j) .^ (2 / (m - 1)));
        membership(class_label + 1) = membership(class_label + 1) + weight;
    end
    y_est(i, :) = membership / sum(membership);
end

For additional insights and examples on implementing FKNN, you can refer to these resources:

1. https://www.mathworks.com/matlabcentral/fileexchange/13358-fuzzy-k-nn
2. https://www.mathworks.com/matlabcentral/fileexchange/21326-fuzzy-k-nn

Hope this helps!