二分类问题使用SVM和BP神经网络的样例【Speech and not-speech detection】

二分类问题使用SVM和BP神经网络的样例【Speech and not-speech detection】 - Carrawayang - 博客园 (cnblogs.com)

数据集采用浙大胡老师的编程作业为例

Assignment 1: Speech and not-speech detection DDL：2017-10-17 Tue. （1）This assignment is carried out by group. You could choose your teammate freely. Each group consists of at most 3 students.

（2）The ‘training.data’ contains the training data. It is from our project to detect whether a person in a video speaks or not. The features are generated in the following way, which may help you making the most of these features. 1、Get the mouth region M from the origin image based on facial landmark detection. 2、Calculate dense optic flow between mouth region of last frame and the current frame and generate a score S that depicts the motion of mouth. 3、Calculate the parameter V which depicts the degree of mouth opening. 4、For frame i, we also calculate the S and V for its previous and next frames. 5、Hence, we generate a 6 dimensional feature vector is X=[Si-1 Si Si+1 Vi-1 Vi Vi+1]. 6、The label is at the end of each line, where +1 represents speaking, and -1 represents not-speaking.

In the training.data, the ratio of positive examples over negative examples is 1:1. Keep this in mind, for if you find your training error or validation error is larger than 50%, that means your solution learns nothing and performs worse than guessing.

（4）You need to write a program to predict speaking or not speaking. For convenience to evaluate your grogram, please use this name for your matlab main function: speakingDetection.m Note about the interface in your function ‘speakingDetection.m’, it should be: function predY= speakingDetection (X) X: The input feature vectors, which is an N6 matrix, where N is the number of feature vectors. predY: The output vector to predict labels of X, which is a N1 vector, and predY(i) = 1 or -1.

Besides MATLAB, you also use Python, as long as you hold the interface protocol above. Note we don’t recommend C/C++.

（5）You can use ANY method to solve this problem.

问题分析

1. 数据解读：training.data数据为N*7的matrix矩阵，其中6维vector向量为输入特征 input feature

2. 数据预处理：将training.data读入，进行dataset的分割，分为6维向量input feature和1维向量label,分割前对数据集进行shuffle，分出测试集以及训练集

3. 模型选择：该问题为数据的分类，采用分类算法可以解决，本文以SVM和BP神经网络为样例

4. 建立相应模型求解问题

5. 调节参数，达到最优解

BP神经网络代码

clc;
clear;
data=importdata('training.data');
P=data(:,1:6);
T=data(:,7);
temp = randperm(size(data,1));
% 训练集——5000个样本
P_train = P(temp(1:5000),:)';
T_train = T(temp(1:5000),:)';
P_test = P(temp(end-50:end),:)';
T_test = T(temp(end-50:end),:)';
N=size(T_test,2);
[pn,minp,maxp,tn,mint,maxt]=premnmx(P_train,T_train);
[pn_,minp_,maxp_,tn_,mint_,maxt_]=premnmx(P_test,T_test);
dx=[-1,1;-1,1;-1,1;-1,1;-1,1;-1,1];
net=newff(dx,[6,10,1]);
net.trainParam.goal = 0;
net.trainParam.epochs = 30000;
net.trainParam.lr = 0.03;
net.trainParam.showWindow = 1;
net = train(net,pn,tn);
an = sim(net,pn_);
a=postmnmx(an,mint_,maxt_);
disp(['mse: ' num2str(mse(T_test-an))]);
count=0;
error=0;
for i=1:N
 if abs(a(i)-T_test(i))<0.2
     count=count+1;
 else
     error=error+1;
 end
end
accuracy=count/(count+error)
figure
plot(1:N,T_test,'b*',1:N,a,'ro')
legend('真实值','预测值')
xlabel('预测样本')
ylabel('实值')

运行结果 accuracy：0.7059 mse: 1.0783

SVM代码[采用LIBSVM]

data=importdata('training.data');
features=data(:,1:6);%特征列表
classlabel=data(:,7);%对应类别
n = randperm(size(features,1));%随机产生训练集和测试集
%% 训练集--70个样本
train_features=features(n(1:44000),:);
train_label=classlabel(n(1:44000),:);
%% 测试集--30个样本
test_features=features(n(44000:end),:);
test_label=classlabel(n(44000:end),:);
%% 数据归一化
[Train_features,PS] = mapminmax(train_features');
Train_features = Train_features'; 
Test_features = mapminmax('apply',test_features',PS); 
Test_features = Test_features';
%% 创建/训练SVM模型
model = svmtrain(train_label,Train_features,'-h 0');
%% SVM仿真测试
[predict_train_label] = svmpredict(train_label,Train_features,model);
[predict_test_label] = svmpredict(test_label,Test_features,model);
%% 打印准确率
compare_train = (train_label == predict_train_label);
accuracy_train = sum(compare_train)/size(train_label,1)*100; 
fprintf('训练集准确率：%f\n',accuracy_train)
compare_test = (test_label == predict_test_label);
accuracy_test = sum(compare_test)/size(test_label,1)*100;
fprintf('测试集准确率：%f\n',accuracy_test)

运行结果

.................*
optimization finished, #iter = 17228
nu = 0.658959
obj = -28684.553581, rho = 4.599546
nSV = 29001, nBSV = 28987
Total nSV = 29001
Accuracy = 71.7273% (31560/44000) (classification)
Accuracy = 71.1948% (435/611) (classification)
训练集准确率：71.727273
测试集准确率：71.194763

结果分析

两种模型按照题目要求可以达到错误率低于50%的要求，相对而言，SVM在该问题上无论是性能还是效果都略高于BP神经网络算法，SVM更适用于小样本的分类问题

 

文件下载：training.data

https://files.cnblogs.com/files/Carraway-Space/training.zip