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
资源内容介绍
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>