MatLab2012b/MatLab2013b分類器大全(svm,knn,隨機森林等)

train_data是訓練特徵數據, train_label是分類標籤。

Predict_label是預測的標籤。

MatLab訓練數據, 得到語義標籤向量 Scores(概率輸出)。

1.邏輯迴歸(多項式MultiNomial logistic Regression)

Factor = mnrfit(train_data, train_label);

Scores = mnrval(Factor, test_data);

scores是語義向量(概率輸出)。對高維特徵，吃不消。

2.隨機森林分類器（Random Forest）

Factor = TreeBagger(nTree, train_data, train_label);

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

scores是語義向量(概率輸出)。實驗中nTree = 500。

效果好，但是有點慢。2500行數據，耗時400秒。500萬行大數據分析，會咋樣？準備好一篇小說慢慢閱讀吧^_^

3.樸素貝葉斯分類（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. 支持向量機SVM分類

Factor = svmtrain(train_data, train_label);

predict_label = svmclassify(Factor, test_data);

不能有語義向量 Scores(概率輸出)

支持向量機SVM(Libsvm)

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

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

5.K近鄰分類器 （KNN）

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

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

不能有語義向量 Scores(概率輸出)

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是近鄰樣本的下標集合，Dist是距離集合。

自己編寫, 實現概率輸出 Scores(概率輸出)

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.集成學習器（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);

效果比預期差了很多。不佳。

7. 判別分析分類器（discriminant analysis classifier）

Factor = ClassificationDiscriminant.fit(train_data, train_label);

Factor = ClassificationDiscriminant.fit(train_data, train_label, 'discrimType', '判別類型:僞線性...');

predict_label = predict(Factor, test_data);

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