我正在创建一个情绪识别程序，并设法生成两种不同的算法/特征以输入 sklearn 的 SVM。我得到了密集的光流数据，将其压缩成一个矩阵并将其输入到 SVM 函数中，同时我对来自面部特征点的跟踪数据进行了同样的处理。
现在，我有两个不同的程序，每个程序都产生不同的准确性，但做的是同一件事: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，该分类器将同时使用这两者来实现更高的分类精度。
基本上，我正在尝试从中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)
包含人脸界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],
...,
[ -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. ],
...,
[ 1.41421356, -0.78539816],
[ 1. , 0. ],
[ 1. , 0. ]]],
[[[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ],
...,
[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ]],
[[ 1. , 1. ],
[ -1.41421356, -0.78539816],
[ -1. , -0. ],
...,
[ 2. , 0. ],
[ 1. , 0. ],
[ -1. , -0. ]],
[[ 0. , 0. ],
[ 1. , 1. ],
[ 0. , 0. ],
...,
[ -4. , -0. ],
[ -3. , -0. ],
[ -2. , -0. ]],
...,
[[ -2.23606798, -1.10714872],
[ -2.23606798, -1.10714872],
[ 2. , 1. ],
...,
[ 0. , 0. ],
[ 1.41421356, 0.78539816],
[ 1.41421356, 0.78539816]],
[[ 0. , 0. ],
[ 0. , 0. ],
[ -1. , -0. ],
...,
[ 1. , 1. ],
[ 0. , 0. ],
[ -1.41421356, 0.78539816]],
[[ 1. , 1. ],
[ 1. , 1. ],
[ 1. , 1. ],
...,
[ 1. , 1. ],
[ 0. , 0. ],
[ 1. , 0. ]]],
[[[ 0. , 0. ],
[ 0. , 0. ],
[ 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],
[ 0. , 0. ],
[ 1.41421356, 0.78539816],
...,
[ -1.41421356, 0.78539816],
[ -1. , -0. ],
[ 0. , 0. ]],
...,
[[ 1. , 1. ],
[ 1. , 1. ],
[ 0. , 0. ],
...,
[ 1. , 1. ],
[ 1. , 1. ],
[ -1.41421356, 0.78539816]],
[[ 1. , 1. ],
[ 2. , 1. ],
[ 2.23606798, 1.10714872],
...,
[ 1. , 1. ],
[ 1. , 1. ],
[ -1.41421356, -0.78539816]],
[[ 1. , 1. ],
[ 1. , 1. ],
[ 1. , 1. ],
...,
[ -2. , -0. ],
[ -2. , -0. ],
[ -1. , -0. ]]],
...,
[[[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ],
...,
[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ]],
[[ 1.41421356, 0.78539816],
[ 1.41421356, 0.78539816],
[ 1.41421356, 0.78539816],
...,
[ 1. , 1. ],
[ 0. , 0. ],
[ 1. , 1. ]],
[[ 5. , 1. ],
[ -4.12310563, 1.32581766],
[ -4.12310563, 1.32581766],
...,
[ 1. , 1. ],
[ 0. , 0. ],
[ 1. , 1. ]],
...,
[[ 3.16227766, 1.24904577],
[ 2. , 1. ],
[ 2. , 1. ],
...,
[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ]],
[[ -3.16227766, 1.24904577],
[ 2. , 1. ],
[ -2.23606798, 1.10714872],
...,
[ 0. , 0. ],
[ 1. , 0. ],
[ 1. , 0. ]],
[[ 1. , 1. ],
[ 1. , 1. ],
[ 1.41421356, 0.78539816],
...,
[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ]]],
[[[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ],
...,
[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ]],
[[ -2.23606798, 0.46364761],
[ -1.41421356, 0.78539816],
[ -2.23606798, 0.46364761],
...,
[ 1. , 0. ],
[ 1. , 0. ],
[ 1. , 1. ]],
[[ -2.23606798, -0.46364761],
[ -1.41421356, -0.78539816],
[ 2. , 1. ],
...,
[ 0. , 0. ],
[ 1. , 0. ],
[ 1. , 0. ]],
...,
[[ 1. , 0. ],
[ 1. , 1. ],
[ -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. ],
[ 0. , 0. ],
[ 0. , 0. ],
...,
[ 0. , 0. ],
[ 0. , 0. ],
[ 0. , 0. ]],
[[ -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. ]],
...,
[[ -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. ],
...,
[ 1. , 0. ],
[ 0. , 0. ],
[ 1. , 0. ]]]])
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)
我的人脸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，我如何将这两个特征结合在一起，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？
最佳答案
看看VotingClassifier .它允许您组合多个分类器，并根据每个分类器的单独预测选择最终预测。
以上假设您可以使用 sklearn 的开发 (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。
关于python - 如何将两个特征/分类器组合成一个统一且更好的分类器？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/31166240/