Classification prediction | MATLAB implements WOA-FS-SVM whale algorithm synchronous optimization feature selection combined with support vector machine classification prediction

Classification prediction | MATLAB implements WOA-FS-SVM whale algorithm synchronous optimization feature selection combined with support vector machine classification prediction

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basic introduction

MATLAB implements WOA-FS-SVM whale algorithm synchronous optimization feature selection combined with support vector machine classification prediction (complete program and data)
WOA whale algorithm synchronous optimization feature selection combined with support vector machine classification prediction, comparison before and after optimization, based on LIBSVM.

programming

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%%  参数设置
% 定义优化参数的个数，在该场景中，优化参数的个数为数据集特征总数 。
%目标函数
fobj = @(x) fun(x,train_wine_labels,train_wine,test_wine_labels,test_wine); 
% 优化参数的个数 特征维度
dim = size(train_wine,2); %特征维度
% 优化参数的取值下限，[0,1],大于0.5为选择该特征，小于0.5为不选择该特征
lb = 0;
ub = 1;

%%  参数设置
pop =10; %数量
Max_iteration=50;%最大迭代次数             
%% 优化(这里主要调用函数)
[Best_score,Best_pos,curve]=WOA(pop,Max_iteration,lb,ub,dim,fobj); 
figure
plot(curve,'linewidth',1.5);
xlabel('迭代次数');
ylabel('适应度值');
title('收敛曲线');
grid on;

c = 2;  
g = 2; 
toc
% 用优化得到的特征进行训练和测试
cmd = ['-s 0 -t 2 ', '-c ', num2str(c), ' -g ', num2str(g), ' -q'];
model = libsvmtrain(train_wine_labels, train_wineNew, cmd);
test_wineNew = test_wine(:,B);
%% SVM网络预测
[predict_labelTrain, accuracyTrain,~] = libsvmpredict(train_wine_labels, train_wineNew, model);
[predict_labelTest, accuracyTest,~] = libsvmpredict(test_wine_labels, test_wineNew, model);

%% 基础SVM预测结果
% 用优化得到的特征进行训练和测试
cmd = ['-s 0 -t 2 ', '-c ', num2str(c), ' -g ', num2str(g), ' -q'];
model = libsvmtrain(train_wine_labels, train_wine, cmd);
%% SVM网络预测
[predict_labelTrain1, accuracyTrain1,~] = libsvmpredict(train_wine_labels, train_wine, model);
[predict_labelTest1, accuracyTest1,~] = libsvmpredict(test_wine_labels, test_wine, model);%% 结果分析

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