Classification prediction | Matlab implements GRNN-Adaboost multi-feature classification prediction

Classification prediction | Matlab implements GRNN-Adaboost multi-feature classification prediction

Effect list

basic introduction

1. Matlab implements GRNN-Adaboost multi-feature classification prediction (Matlab complete program and data)
2. Multi-feature input model can be used by directly replacing the data.
3. The language is matlab. Classification effect diagram, confusion matrix diagram.
4. Classification effect diagram, confusion matrix diagram.
5.Data classification prediction of BP-Adaboost.
The operating environment is matlab2018 and above.

research content

GRNN-Adaboost is a method that combines two machine learning technologies, GRNN and AdaBoost, to improve the performance and robustness of the model. Specifically, AdaBoost is an ensemble learning method that combines multiple weak classifiers to form a strong classifier, where each classifier is trained for different data sets and feature representations. The basic idea of ​​the GRNN-AdaBoost algorithm is to use GRNN as the base model and use the AdaBoost algorithm to enhance it. Specifically, we can train multiple GRNN models, each using different datasets and feature representations, and then combine their predictions to form a more accurate and robust model.

programming

• For the complete program and data download method, please send a private message to the blogger to reply to Matlab to implement data classification prediction based on GRNN-Adaboost .

%%  数据归一化
[p_train, ps_input] = mapminmax(P_train, 0, 1);
p_test = mapminmax('apply', P_test, ps_input );
t_train = T_train;
t_test  = T_test ;

%%  输出编码
t_train = ind2vec(t_train);
t_test  = ind2vec(t_test );



%%  数据反归一化
T_sim1 = vec2ind(t_sim1);
T_sim2 = vec2ind(t_sim2);

%%  性能评价
error1 = sum((T_sim1 == T_train)) / M * 100 ;
error2 = sum((T_sim2 == T_test )) / N * 100 ;

%%  绘图
figure
plot(1: M, T_train, 'r-*', 1: M, T_sim1, 'b-o', 'LineWidth', 1)
legend('真实值', '预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {
    
    '训练集预测结果对比'; ['准确率=' num2str(error1) '%']};
title(string)
grid

figure
plot(1: N, T_test, 'r-*', 1: N, T_sim2, 'b-o', 'LineWidth', 1)
legend('真实值', '预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {
    
    '测试集预测结果对比'; ['准确率=' num2str(error2) '%']};
title(string)
grid

References

[1] https://blog.csdn.net/kjm13182345320/article/details/128163536?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128151206?spm=1001.2014.3001.5502