MatLab2012b/MatLab2013b分类器大全(svm,knn,随机森林等)

train_data是训练特征数据, train_label是分类标签。

Predict_label是预测的标签。

MatLab训练数据, 得到语义标签向量 Scores(概率输出)。

1.逻辑回归(多项式MultiNomial logistic Regression)

Factor = mnrfit(train_data, train_label);

Scores = mnrval(Factor, test_data);

scores是语义向量(概率输出)。对高维特征，吃不消。

2.随机森林分类器（Random Forest）

Factor = TreeBagger(nTree, train_data, train_label);

[Predict_label,Scores] = predict(Factor, test_data);

scores是语义向量(概率输出)。实验中nTree = 500。

效果好，但是有点慢。2500行数据，耗时400秒。500万行大数据分析，会咋样？准备好一篇小说慢慢阅读吧^_^

3.朴素贝叶斯分类（Naive Bayes）

Factor = NaiveBayes.fit(train_data, train_label);

Scores = posterior(Factor, test_data);

[Scores,Predict_label] = posterior(Factor, test_data);

Predict_label = predict(Factor, test_data);

accuracy = length(find(predict_label == test_label))/length(test_label)*100;

效果不佳。

4. 支持向量机SVM分类

Factor = svmtrain(train_data, train_label);

predict_label = svmclassify(Factor, test_data);

不能有语义向量 Scores(概率输出)

支持向量机SVM(Libsvm)

Factor = svmtrain(train_label, train_data, '-b 1');

[predicted_label, accuracy, Scores] = svmpredict(test_label, test_data, Factor, '-b 1');

5.K近邻分类器 （KNN）

predict_label = knnclassify(test_data, train_data,train_label, num_neighbors);

accuracy = length(find(predict_label == test_label))/length(test_label)*100;

不能有语义向量 Scores(概率输出)

IDX = knnsearch(train_data, test_data);

IDX = knnsearch(train_data, test_data, 'K', num_neighbors);

[IDX, Dist] = knnsearch(train_data, test_data, 'K', num_neighbors);

IDX是近邻样本的下标集合，Dist是距离集合。

自己编写, 实现概率输出 Scores(概率输出)

Matlab 2012新版本:

Factor = ClassificationKNN.fit(train_data, train_label, 'NumNeighbors', num_neighbors);

predict_label = predict(Factor, test_data);

[predict_label, Scores] = predict(Factor, test_data);

6.集成学习器（Ensembles for Boosting, Bagging, or Random Subspace）

Matlab 2012新版本:

Factor = fitensemble(train_data, train_label, 'AdaBoostM2', 100, 'tree');

Factor = fitensemble(train_data, train_label, 'AdaBoostM2', 100, 'tree', 'type', 'classification');

Factor = fitensemble(train_data, train_label, 'Subspace', 50, 'KNN');

predict_label = predict(Factor, test_data);

[predict_label, Scores] = predict(Factor, test_data);

效果比预期差了很多。不佳。

7. 判别分析分类器（discriminant analysis classifier）

Factor = ClassificationDiscriminant.fit(train_data, train_label);

Factor = ClassificationDiscriminant.fit(train_data, train_label, 'discrimType', '判别类型:伪线性...');

predict_label = predict(Factor, test_data);

[predict_label, Scores] = predict(Factor, test_data);