XGBoost、RF随机森林算法MATLAB实现

% 加载并预处理训练数据

opts1 = detectImportOptions('附件一AE.xlsx', 'PreserveVariableNames', true);

train_data = readtable('附件一AE.xlsx', opts1);

train_data.Time = datetime(train_data.time, 'InputFormat', 'yyyy-MM-dd HH:mm:ss');

% 特征提取和标签准备

windowSize = 20;

numWindows = floor(height(train_data) / windowSize);

amplitudes = zeros(numWindows, 1);

noiseLevels = zeros(numWindows, 1);

labels = false(numWindows, 1);

for i = 1:numWindows

    startIdx = (i-1) * windowSize + 1;

    endIdx = startIdx + windowSize - 1;

    windowData = train_data.AE(startIdx:endIdx);

    amplitudes(i) = max(windowData) - min(windowData);

    noiseLevels(i) = std(windowData);

    labels(i) = any(strcmp(train_data.class(startIdx:endIdx), 'C'));

end

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