分類預測 | Matlab實現(xiàn)GA-RF遺傳算法優(yōu)化隨機森林多輸入分類預測

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基本介紹

Matlab實現(xiàn)GA-RF遺傳算法優(yōu)化隨機森林多輸入分類預測（完整源碼和數據）
Matlab實現(xiàn)GA-RF遺傳算法優(yōu)化隨機森林分類預測，多輸入單輸出模型。GA-RF分類預測模型
多特征輸入單輸出的二分類及多分類模型。程序內注釋詳細，直接替換數據就可以用。程序語言為matlab，程序可出分類效果圖，混淆矩陣圖。優(yōu)化隨機森林樹木棵樹何深度。

程序設計

• 完整源碼和數據下載：Matlab實現(xiàn)GA-RF遺傳算法優(yōu)化隨機森林多輸入分類預測

%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  清空環(huán)境變量
clc;
clear;
warning off
close all
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  添加路徑
addpath("Toolbox\")
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  讀取數據
res = xlsread('數據集.xlsx');
%%  性能評價
error1 = sum((T_sim1' == T_train)) / M * 100 ;
error2 = sum((T_sim2' == T_test )) / N * 100 ;
%-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  繪圖
figure
plot(1: M, T_train, 'r-*', 1: M, T_sim1, 'b-o', 'LineWidth', 1)
legend('真實值', '預測值')
xlabel('預測樣本')
ylabel('預測結果')
string = {'訓練集預測結果對比'; ['準確率=' num2str(error1) '%']};
title(string)
grid

figure
plot(1: N, T_test, 'r-*', 1: N, T_sim2, 'b-o', 'LineWidth', 1)
legend('真實值', '預測值')
xlabel('預測樣本')
ylabel('預測結果')
string = {'測試集預測結果對比'; ['準確率=' num2str(error2) '%']};
title(string)
grid
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  混淆矩陣
if flag_conusion == 1

    figure
    cm = confusionchart(T_train, T_sim1);
    cm.Title = 'Confusion Matrix for Train Data';
    cm.ColumnSummary = 'column-normalized';
    cm.RowSummary = 'row-normalized';
    
    figure
    cm = confusionchart(T_test, T_sim2);
    cm.Title = 'Confusion Matrix for Test Data';
    cm.ColumnSummary = 'column-normalized';
    cm.RowSummary = 'row-normalized';
end

參考資料

[1] https://download.csdn.net/download/kjm13182345320/87899283?spm=1001.2014.3001.5503
[2] https://download.csdn.net/download/kjm13182345320/87899230?spm=1001.2014.3001.5503文章來源地址http://www.zghlxwxcb.cn/news/detail-731970.html

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