GA与PSO优化RF树结构和叶子数的多维输入单维输出MATLAB预测模型：附详细注释，可生成可视化报告,基于GA和PSO优化的RF多维输入单维输出拟合预测模型：详细注释、图形输出与评价指标打印的MAT

OLCTmlUhnsdpZIP和优化的树数和叶子数做多维输入单维输出拟合预测模  7.3MB

资源文件列表:

ZIP 和优化的树数和叶子数做多维输入单维输出拟合预测模 大约有15个文件
  1. 1.jpg 831.17KB
  2. 2.jpg 263.84KB
  3. 3.jpg 189.46KB
  4. 4.jpg 537.61KB
  5. 5.jpg 225.17KB
  6. 与优化模型多维输入.html 2.7MB
  7. 与优化模型多维输入单维输出拟合预测技术解析一背景.docx 48.03KB
  8. 以下是一个使用语言编写的程序用于遗传.docx 50.43KB
  9. 和优化模型在多维输入单维输出拟合.docx 48.62KB
  10. 和优化模型提升预测精度与稳定性随着数据科学和技术.html 2.7MB
  11. 和优化的树数和叶子数做多维输入单维输出拟合.html 2.7MB
  12. 在机器学习和数据分析领域遗传算法和粒.docx 15.93KB
  13. 基于和优化算法的树数与叶子数调.docx 48.12KB
  14. 遗传算法和粒子群算法是两种常用的优化算法它们可以被.docx 24.28KB
  15. 题目用与优化随机森林的树数和叶子数实现.docx 48.62KB

资源介绍:

GA与PSO优化RF树结构和叶子数的多维输入单维输出MATLAB预测模型：附详细注释，可生成可视化报告,基于GA和PSO优化的RF多维输入单维输出拟合预测模型：详细注释、图形输出与评价指标打印的MATLAB程序,GA和PSO优化RF的树数和叶子数，做多维输入单维输出拟合预测模型。 程序内有详细注释，易于学习，直接替数据可用。 可以出特征重要性排序图，真实值和预测值对比图，可打印多种评价指标。 程序是MATLAB语言。 ,GA; PSO; RF; 树数和叶子数优化; 多维输入单维输出拟合预测模型; 详细注释; 直接替换数据可用; 特征重要性排序图; 真实值与预测值对比图; 多种评价指标。,基于GA和PSO优化的RF模型：多维输入单维输出预测与评估系统
<link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90429525/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90429525/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">以下是一个使用<span class="_ _0"> </span><span class="ff2">MATLAB<span class="_ _0"> </span></span>语言编写的程序，<span class="_ _1"></span>用于<span class="_ _0"> </span><span class="ff2">GA</span>（遗传算法）<span class="_ _1"></span>和<span class="_ _0"> </span><span class="ff2">PSO</span>（粒子群优化）<span class="_ _1"></span>来</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">优化<span class="_ _2"></span>随机<span class="_ _2"></span>森林<span class="_ _2"></span>（<span class="ff2">RF<span class="_ _2"></span></span>）模<span class="_ _2"></span>型，<span class="_ _2"></span>该模<span class="_ _2"></span>型处<span class="_ _2"></span>理多<span class="_ _2"></span>维输<span class="_ _2"></span>入单<span class="_ _2"></span>维输<span class="_ _2"></span>出的<span class="_ _2"></span>拟合<span class="_ _2"></span>预测<span class="_ _2"></span>问题<span class="_ _2"></span>。同<span class="_ _2"></span>时，<span class="_ _2"></span>该程<span class="_ _2"></span>序</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">将提供特征重要性排序图、真实值与预测值对比图以及多种评价指标的可打印输出。</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">导入或创建数据集</span></div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">假设</span> <span class="_ _3"> </span>X <span class="_ _3"> </span><span class="ff1">为输入数据矩阵，</span>y <span class="_ _3"> </span><span class="ff1">为输出标签向量</span></div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">% X <span class="_ _3"> </span><span class="ff1">和</span> <span class="_ _3"> </span>y <span class="_ _3"> </span><span class="ff1">的维度应匹配，且</span> <span class="_ _3"> </span>y <span class="_ _3"> </span><span class="ff1">为单维输出</span></div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">例如：</span>X = rand(100, 10); y = rand(100, 1);</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">定义<span class="_ _0"> </span></span>RF<span class="_ _3"> </span><span class="ff1">模型的树数和叶子数初始范围</span></div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">minTrees = 10;</div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">maxTrees = 200;</div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">minLeaves = 2;</div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">maxLeaves = 50;</div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">使用<span class="_ _0"> </span></span>GA<span class="_ _0"> </span><span class="ff1">进行树数优化</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">ga_params = struct('FitnessFunction', @(treeNum) evalRFPerformance(X, y, treeNum), ...</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>'InitialPopulationSize', 50, ...</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>'MaxGenerations', 100, ...</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>'BinaryMutationFcn', {@mutation_flipbit, 0.1}, ...</div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>'CrossoverFcn', {@crossover_uniform, 1}, ...</div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>'Termination', @isbest);</div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">tree_opt_ga = ga(minLeaves:maxLeaves, ga_params);</div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">best_tree_num = tree_opt_ga.X;</div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">使用<span class="_ _0"> </span></span>PSO<span class="_ _0"> </span><span class="ff1">进行叶子数优化</span></div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">pso_params = struct('Swarmsize', 20, ...</div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _5"> </span>'InertiaWeight', 0.9, ...</div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _5"> </span>'CognitiveComponent', 2, ...</div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _5"> </span>'SocialComponent', 2, ...</div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _5"> </span>'FitnessFunction', <span class="_ _6"> </span>@(leaves) <span class="_ _6"> </span>evalRFPerformance(X, <span class="_ _6"> </span>y, <span class="_ _6"> </span>best_tree_num, </div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">leaves));</div><div class="t m0 x1 h2 y1e ff2 fs0 fc0 sc0 ls0 ws0">leaves_opt_pso = pso(minLeaves:maxLeaves, pso_params);</div><div class="t m0 x1 h2 y1f ff2 fs0 fc0 sc0 ls0 ws0">best_leaves = leaves_opt_pso.X;</div><div class="t m0 x1 h2 y20 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">基于<span class="_ _0"> </span></span>GA<span class="_ _0"> </span><span class="ff1">和<span class="_ _0"> </span></span>PSO<span class="_ _0"> </span><span class="ff1">的结果来建立最优的<span class="_ _0"> </span></span>RF<span class="_ _3"> </span><span class="ff1">模型，并进行测试集上的拟合预测。</span></div><div class="t m0 x1 h2 y21 ff2 fs0 fc0 sc0 ls0 ws0">nTrees = best_tree_num; % <span class="_ _3"> </span><span class="ff1">根据<span class="_ _3"> </span></span>GA<span class="_"> </span><span class="ff1">得到最优树数</span></div><div class="t m0 x1 h2 y22 ff2 fs0 fc0 sc0 ls0 ws0">nLeaves = best_leaves; % <span class="_ _3"> </span><span class="ff1">根据<span class="_ _3"> </span></span>PSO<span class="_"> </span><span class="ff1">得到最优叶子数</span></div><div class="t m0 x1 h2 y23 ff2 fs0 fc0 sc0 ls0 ws0">rfModel = fitensemble(X, y, 'TreeBaggedEnsemble', nTrees, nLeaves); % <span class="_ _3"> </span><span class="ff1">创建随机森林模型</span></div><div class="t m0 x1 h2 y24 ff2 fs0 fc0 sc0 ls0 ws0">testX = X(:,[some indices]); % <span class="_ _3"> </span><span class="ff1">选择一部分数据作为测试集（这里需要替换为实际测试集）</span></div><div class="t m0 x1 h2 y25 ff2 fs0 fc0 sc0 ls0 ws0">testY = predict(rfModel, testX); % <span class="_ _3"> </span><span class="ff1">使用模型进行预测</span></div><div class="t m0 x1 h2 y26 ff2 fs0 fc0 sc0 ls0 ws0">yActual = y; % <span class="_ _3"> </span><span class="ff1">实际输出标签（替换为测试集的标签）</span></div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>
100+评论
captcha
    相关资源