smote算法matlab代码

下面是使用SMOTE算法进行过采样的Matlab代码：

% load your dataset
data = load('your_dataset.mat');
X = data.X;
y = data.y;

% apply SMOTE algorithm
smote_perc = 200; % percentage of SMOTE oversampling
k = 5; % number of nearest neighbors to consider
synth_samples = smote(X, y, smote_perc, k);

% combine original and synthetic samples
X_resampled = [X; synth_samples];
y_resampled = [y; ones(size(synth_samples, 1), 1)];

% train your model with the resampled data
model = trainModel(X_resampled, y_resampled);

其中，`smote.m`是SMOTE算法的函数，`trainModel`是你训练模型的函数，可以根据你选择的模型进行修改。

相关问题

smote过采样matlab代码

SMOTE（Synthetic Minority Over-sampling Technique）是一种过采样算法，用于处理分类问题中的类别不平衡情况。在MATLAB中，我们可以使用以下代码实现SMOTE过采样：

function [SMOTE_data, SMOTE_labels] = smote(data, labels, k, minority_class)
    minority_indices = find(labels == minority_class);
    minority_data = data(minority_indices, :);
    minority_size = length(minority_data);
    
    synthetic_data = zeros(k * minority_size, size(minority_data, 2));
    synthetic_labels = zeros(k * minority_size, 1);
    
    for i = 1:minority_size
        nn_indices = knnsearch(data, minority_data(i, :), 'K', k+1); % 找到k个最近邻样本的索引（包括自身）
        nn_indices(nn_indices == minority_indices(i)) = []; % 去除自身的索引
        
        for j = 1:k
            dif = data(nn_indices(j), :) - minority_data(i, :); % 计算差值
            gap = rand(); % 随机生成0到1之间的比例
            synthetic_data((i-1)*k + j, :) = minority_data(i, :) + gap * dif; % 生成合成样本
            synthetic_labels((i-1)*k + j) = minority_class; % 合成样本的标签为原始类别
        end
    end
    
    SMOTE_data = [data; synthetic_data]; % 合并原始数据和合成数据
    SMOTE_labels = [labels; synthetic_labels]; % 合并原始标签和合成标签
end

这段代码中，首先找到原始数据中属于少数类别的样本，然后对每个少数类别样本，找到它的k个最近邻样本。接着，根据原始样本和最近邻样本的差值，生成k个合成样本。每个合成样本的特征值由原始样本和差值的按比例加权得到，合成样本的标签与原始样本相同。随后，将原始数据和合成数据以及对应的标签进行合并，得到SMOTE过采样后的数据和标签。

通过以上的代码实现，我们可以使用MATLAB来进行SMOTE过采样处理，从而解决类别不平衡问题。

给出一个smote算法的matlab示例

以下是一个基于MATLAB的SMOTE算法的示例代码：

% load dataset
load data.mat

% split into training and testing set
cv = cvpartition(size(X,1), 'HoldOut', 0.3);
Xtrain = X(cv.training,:);
Ytrain = Y(cv.training,:);
Xtest = X(cv.test,:);
Ytest = Y(cv.test,:);

% SMOTE
smoteAmount = 200; % set the number of new synthetic samples to be generated
[~,idxMinority] = findGroups(Ytrain == 1); % find indices of minority class samples
Xmin = Xtrain(idxMinority,:); % extract minority class samples
Xsynth = smote(Xmin, smoteAmount); % generate synthetic samples using SMOTE
Ysynth = repmat(1, size(Xsynth,1), 1); % assign class label

% combine with original training set
Xtrain = [Xtrain; Xsynth];
Ytrain = [Ytrain; Ysynth];

% train model on SMOTE-enhanced training set
model = fitcdiscr(Xtrain, Ytrain);

% test model on testing set
Ypred = predict(model, Xtest);

% calculate accuracy
accuracy = sum(Ypred == Ytest) / numel(Ytest);

% display results
fprintf('Accuracy on testing set: %f\n', accuracy);

% plotting: optional
figure;
gscatter(Xtrain(:,1), Xtrain(:,2), Ytrain);
hold on;
gscatter(Xsynth(:,1), Xsynth(:,2), Ysynth);
title('SMOTE-enhanced training set');
legend('Class 0', 'Class 1', 'Synthetic samples');
hold off;

% SMOTE function
function Xsynth = smote(Xmin, smoteAmount)
k = 5; % number of nearest neighbors to consider
Xsynth = zeros(smoteAmount, size(Xmin,2)); % preallocate matrix for synthetic samples

for i = 1:smoteAmount
ind = randi(size(Xmin,1)); % random index from the minority class
x = Xmin(ind,:); % extract a sample x from the minority class samples
neighbors = knnsearch(Xmin, x, 'K', k+1); % find k+1 nearest neighbors (including x)
neighbors(neighbors == ind) = []; % remove x from nearest neighbors
nnInd = randi(numel(neighbors)); % random index from nearest neighbors

% compute difference vector between x and its random nearest neighbor
diffVec = Xmin(neighbors(nnInd),:) - x;
% generate random weight for difference vector
weight = rand();
% add weighted difference vector to x to generate synthetic sample
Xsynth(i,:) = x + (weight * diffVec);
end
end