smote算法matlab代码

以下是使用MATLAB实现SMOTE算法的代码示例：

function [X_resampled, y_resampled] = smote(X, y, k, minority_class, N)

% X: 特征矩阵
% y: 标签
% k: K近邻数
% minority_class: 少数类标签
% N: 合成新样本数量

X_minority = X(y == minority_class, :); % 获取少数类样本
num_minority = size(X_minority, 1); % 少数类样本数量
X_resampled = X;
y_resampled = y;

for i = 1:N
    % 随机选择一个少数类样本
    idx = randi(num_minority);
    x = X_minority(idx, :);
    
    % 找到该样本的K近邻
    dists = pdist2(x, X);
    [sorted_dists, sorted_idx] = sort(dists);
    knn_idx = sorted_idx(2:k+1);
    
    % 随机选择一个K近邻
    knn_X = X(knn_idx, :);
    knn_idx = knn_idx(randi(k));
    knn_x = X(knn_idx, :);
    
    % 在该样本与选定K近邻之间插值
    lambda = rand;
    new_x = x + lambda * (knn_x - x);
    
    % 添加新样本
    X_resampled = [X_resampled; new_x];
    y_resampled = [y_resampled; minority_class];
end

end

使用示例：

% 导入数据
data = readtable('data.csv');
X = data{:, 1:end-1};
y = data{:, end};

% 运行SMOTE算法
[X_resampled, y_resampled] = smote(X, y, 5, 1, 100);

% 训练模型
model = fitcsvm(X_resampled, y_resampled);

% 预测
y_pred = predict(model, X_test);