我正在创建一个情感识别程序，并设法生成两种不同的算法/功能，以提供给sklearn的SVM。我获得了密集光流数据，将其压缩成矩阵并将其输入SVM功能，同时我也使用面部地标跟踪数据。
现在，我有两个不同的程序，每个程序产生不同的准确性，但做同样的事情：根据面部运动识别情绪。
我现在的目标是将密集光流和面部地标特征/分类器结合在一起并将它们联合起来，以获得一个更好的分类器，使用这两个分类器来实现更高的分类准确度。
基本上，我试图从这里重新创建一个分类器： http://cgit.nutn.edu.tw:8080/cgit/PaperDL/LZJ_120826151743.PDF
Confusion Matrix for Dense Optical Flow:
[[27 22 0 0]
[ 0 57 1 0]
[ 0 12 60 0]
[ 0 9 3 68]]
Accuracy: 80-90% range
Confusion Matrix for Facial Landmarks:
[[27 10 5 2]
[ 7 44 5 3]
[ 6 14 33 1]
[ 1 13 1 60]]
Accuracy: 60-72% range
包含密集光流数据的矩阵的矩阵结构：
>>> main.shape
(646, 403680)
>>> main
array([[ -1.18353125e-03, -2.41295085e-04, -1.88367767e-03, ...,
-5.19892928e-05, 8.53588153e-06, -3.90818786e-05],
[ 6.32877424e-02, -7.24349543e-02, 8.19472596e-02, ...,
-4.71765925e-05, 5.41217596e-05, -3.12083102e-05],
[ -1.66368652e-02, 2.50510368e-02, -6.03965335e-02, ...,
-9.85100851e-05, -7.69595645e-05, -7.09727174e-05],
...,
[ -3.44874617e-03, 5.31123485e-03, -8.47499538e-03, ...,
-2.77953018e-06, -2.96417579e-06, -1.51305017e-06],
[ 3.24894954e-03, 5.05338283e-03, 3.91049543e-03, ...,
-3.23493354e-04, 1.30995919e-04, -3.06804082e-04],
[ 7.82454386e-03, 1.69946514e-02, 8.11014231e-03, ...,
-1.02751539e-03, 7.68289610e-05, -7.82517891e-04]], dtype=float32)
包含人脸地标跟踪信息的矩阵的矩阵结构：
>>> main.shape
(646, 17, 68, 2)
>>> main
array([[[[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ],
...,
[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ]],
[[ -2.23606798, -1.10714872],
[ -2.23606798, -1.10714872],
[ 3. , 1. ],
...,
[ 1.41421356, 0.78539816],
[ 1.41421356, 0.78539816],
[ 1. , 0. ]],
[[ 2.82842712, -0.78539816],
[ 2.23606798, -1.10714872],
[ 2.23606798, -1.10714872],
...,
[ -1. , -0. ],
[ -1. , -0. ],
[ -1. , -0. ]],
...,
[[ 2. , 1. ],
[ -2.23606798, 1.10714872],
[ -3.16227766, 1.24904577],
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[ -1. , -0. ],
[ -1.41421356, 0.78539816],
[ -1. , -0. ]],
[[ -1.41421356, -0.78539816],
[ 1. , 1. ],
[ -1.41421356, -0.78539816],
...,
[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ]],
[[ 3. , 1. ],
[ 4. , 1. ],
[ 4. , 1. ],
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[ 1.41421356, -0.78539816],
[ 1. , 0. ],
[ 1. , 0. ]]],
[[[ 0. , 0. ],
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[ 0. , 0. ],
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[[ 1. , 1. ],
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...,
[[ -2.23606798, -1.10714872],
[ -2.23606798, -1.10714872],
[ 2. , 1. ],
...,
[ 0. , 0. ],
[ 1.41421356, 0.78539816],
[ 1.41421356, 0.78539816]],
[[ 0. , 0. ],
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[ -1. , -0. ],
...,
[ 1. , 1. ],
[ 0. , 0. ],
[ -1.41421356, 0.78539816]],
[[ 1. , 1. ],
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[ 1. , 1. ],
...,
[ 1. , 1. ],
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[[[ 0. , 0. ],
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[ 0. , 0. ],
...,
[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ]],
[[ 3.16227766, 1.24904577],
[ 2.23606798, 1.10714872],
[ 2.23606798, 1.10714872],
...,
[ -1.41421356, -0.78539816],
[ -1. , -0. ],
[ -1.41421356, 0.78539816]],
[[ -1.41421356, 0.78539816],
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[ 1.41421356, 0.78539816],
...,
[ -1.41421356, 0.78539816],
[ -1. , -0. ],
[ 0. , 0. ]],
...,
[[ 1. , 1. ],
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...,
[ 1. , 1. ],
[ 1. , 1. ],
[ -1.41421356, 0.78539816]],
[[ 1. , 1. ],
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[ 2.23606798, 1.10714872],
...,
[ 1. , 1. ],
[ 1. , 1. ],
[ -1.41421356, -0.78539816]],
[[ 1. , 1. ],
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[ 1. , 1. ],
...,
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...,
[[[ 0. , 0. ],
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[[ 1.41421356, 0.78539816],
[ 1.41421356, 0.78539816],
[ 1.41421356, 0.78539816],
...,
[ 1. , 1. ],
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[ 1. , 1. ]],
[[ 5. , 1. ],
[ -4.12310563, 1.32581766],
[ -4.12310563, 1.32581766],
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[[ 3.16227766, 1.24904577],
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[[ -3.16227766, 1.24904577],
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[ -2.23606798, 1.10714872],
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[[ 1. , 1. ],
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[ 1.41421356, 0.78539816],
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[[[ 0. , 0. ],
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[[ -2.23606798, 0.46364761],
[ -1.41421356, 0.78539816],
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[[ -2.23606798, -0.46364761],
[ -1.41421356, -0.78539816],
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[[ 1. , 0. ],
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[ -2.23606798, -1.10714872],
...,
[ 19.02629759, 1.51821327],
[ 19. , 1. ],
[-19.10497317, -1.46591939]],
[[ 3.60555128, 0.98279372],
[ 3.60555128, 0.5880026 ],
[ 5. , 0.64350111],
...,
[ 7.28010989, -1.29249667],
[ 7.61577311, -1.16590454],
[ 8.06225775, -1.05165021]],
[[ -7.28010989, 1.29249667],
[ -5. , 0.92729522],
[ -5.83095189, 0.5404195 ],
...,
[ 20.09975124, 1.47112767],
[ 21.02379604, 1.52321322],
[-20.22374842, -1.42190638]]],
[[[ 0. , 0. ],
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...,
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[[ -1.41421356, 0.78539816],
[ -2.23606798, 1.10714872],
[ 2. , 1. ],
...,
[ 1. , 1. ],
[ 1. , 0. ],
[ 2.23606798, -0.46364761]],
[[ 1. , 0. ],
[ 1.41421356, 0.78539816],
[ 1. , 1. ],
...,
[ 0. , 0. ],
[ 1. , 1. ],
[ 0. , 0. ]],
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[[ -1.41421356, -0.78539816],
[ 0. , 0. ],
[ 1. , 1. ],
...,
[ 1. , 0. ],
[ 1. , 0. ],
[ 1. , 0. ]],
[[ 1. , 1. ],
[ -1. , -0. ],
[ 1. , 1. ],
...,
[ -1. , -0. ],
[ 0. , 0. ],
[ -1. , -0. ]],
[[ 0. , 0. ],
[ 1.41421356, -0.78539816],
[ -1. , -0. ],
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[ 1. , 0. ],
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我的密集光流分类器代码：
features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(main, target, test_size = 0.4)
# Determine amount of time to train
t0 = time()
model = SVC(probability=True)
#model = SVC(kernel='poly')
#model = GaussianNB()
model.fit(features_train, labels_train)
print 'training time: ', round(time()-t0, 3), 's'
# Determine amount of time to predict
t1 = time()
pred = model.predict(features_test)
我的脸部地标跟踪分类器代码：
features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(main.reshape(len(main), -1), target, test_size = 0.4)
# Determine amount of time to train
t0 = time()
#model = SVC()
model = SVC(kernel='linear')
#model = GaussianNB()
model.fit(features_train, labels_train)
# Determine amount of time to predict
t1 = time()
pred = model.predict(features_test)
在sklearn（或一般的机器学习）中，如何将这两个功能组合在一起，以创建一个统一且更好的分类器，在训练和预测时将这两个信息考虑在内？
答案 0 :(得分：3)
看看VotingClassifier。它允许您组合多个分类器，并根据每个分类器的各个预测选择最终预测。
以上假设你可以使用sklearn的dev（0.17）版本。如果没有，您可以将VotingClassifier源复制到您的代码中：https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/voting_classifier.py。代码非常简单。
答案 1 :(得分：0)
您可以根据Daniel的建议构建单独的分类器。但是，您可以考虑连接两个数据集：
main_dense_optical
main_face_landmark = main_face_landmark.reshape( len(main_face_landmark), -1 )
main = np.concatenate( [main_dense_optical, main_face_landmark], axis=1 )
# Code for train/test, training, evaluating here