Question

问题：我需要训练一个分类器（在matlab中）来分类多级信号噪声。

因此，我使用fitcecoc在matlab中训练了一个多类SVM，并获得了92％的准确度。

然后我在python中使用sklearn.svm.svc训练了一个多类SVM，但似乎无论如何摆弄参数，我都无法达到69％以上的准确度。

30％的数据被阻止并用于验证培训。混淆矩阵可以在下面看到。

因此，如果任何人对svm.svc多类培训有一些经验或建议，并且可以在我的代码中看到问题，或者有一个建议，那将非常感激。

Python代码：

import numpy as np
from sklearn import svm
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
#from sklearn import preprocessing

#### SET fitting parameters here
C = 100
gamma = 1e-8

#### SET WEIGHTS HERE
C0_Weight = 1*C
C1_weight = 1*C
C2_weight = 1*C
C3_weight = 1*C
C4_weight = 1*C
#####


X = np.genfromtxt('data/features.csv', delimiter=',')
Y = np.genfromtxt('data/targets.csv', delimiter=',')

print 'feature data is of size: ' + str(X.shape)
print 'target data is of size: ' + str(Y.shape)

# SPLIT X AND Y INTO TRAINING AND TEST SET
test_size = 0.3
X_train, x_test, Y_train, y_test = train_test_split(X, Y,         
... test_size=test_size, random_state=0)

svc = svm.SVC(C=C,kernel='rbf', gamma=gamma, class_weight = {0:C0_Weight, 
... 1:C1_weight, 2:C2_weight, 3:C3_weight, 4:C4_weight},cache_size = 1000)

svc.fit(X_train, Y_train)
scores = cross_val_score(svc, X_train, Y_train, cv=10)
print scores
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

Out = svc.predict(x_test)

np.savetxt("data/testPredictions.csv", Out, delimiter=",")
np.savetxt("data/testTargets.csv", y_test, delimiter=",")

# calculate accuracy in test data
Hits = 0
HitsOverlap = 0
for idx, val in enumerate(Out):
    Hits += int(y_test[idx]==Out[idx])
    HitsOverlap += int(y_test[idx]==Out[idx]) + int(y_test[idx]==
    ... (Out[idx]-1)) + int(y_test[idx]==(Out[idx]+1))

print "Accuracy in testset: ", Hits*100/(11595*test_size)
print "Accuracy in testset w. overlap: ", HitsOverlap*100/(11595*test_size)

那些好奇我如何获得参数，他们被发现与GridSearchCV（并将准确度从40％提高到69）

非常感谢任何帮助或建议。

Answer 1

在拉了我的头发之后，答案就在这里找到：http://neerajkumar.org/writings/svm/

当使用StandardScaler（）缩放输入时，svm.svc现在可以为matlab生成出色的结果!!

Sklearn SVM与Matlab SVM

1 个答案: