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Although this scale is not universally accepted, it renders a panorama on agreement levels in the classification. Landis and Koch (1977) proposed a scale and then, values ranging from 0 to 0.20 mean a slight agreement from 0.21 to 0.40 represent a fair agreement from 0.41 to 0.60 indicate a moderate agreement from 0.61 to 0.80 imply a substantial agreement and values between 0.81 and 1.00 represent an almost perfect agreement. Consequently, when the number of cases is 10 trials, the chance level should be modified to 70% ( Müller-Putz et al., 2008).
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For example, in a 2-class problem the chance level is 50%, but with a confidence level depending on the number of cases. However, these parameters should be carefully analyzed because they depend on the number of classes and on the number of cases. The ACC is the probability of performing a correct classification. The accuracy on the classification (ACC) and the error rate (ER = 1 − ACC) are the two more common parameters used for reporting the performance of BCI systems because they are easy to interpret and to compare. Instead, some parameters generally extracted from the confusion matrix are commonly used. While a confusion matrix contains all information of the outcome of a classifier, they are rarely used for reporting results in BCI field because they are difficult to compare and discuss. An asymmetric confusion matrix can reveal a biased classifier. Instead of the number of cases, confusion matrices can present the percentage values. The total number of cases is N = ∑ i = 1 M ∑ j = 1 M n i j. Hence, the elements in the diagonal ( n ii) are the elements correctly classified, while the elements out of the diagonal are misclassified. The elements n ij in the confusion matrix ( i is row identifier and j is the column identifier) indicate the cases belonging to i that had been classified as j. 2.įalse positive: incorrectly classified or detected. True positive: correctly classified or detected. Confusion matrix in a 2-class classification (A) and in a 4-class classification problem (B).
#ARE THERE CONFUSION MATRIX IN MATLAB R2015A SERIES#
This study would provide reference for human motion recognition using other time series signals and further expand the application fields of CNN.Figure 1.7. Compared with the common classifier performing the classification with hand-crafted features, the CNN-SVM combined model could automatically extract features using CNN, and better improved the generalization ability of CNN and the classification accuracy by means of combining the SVM. In this paper, we detected 4-channel mechanomyography (MMG) signals from the thigh muscles and fed them in the form of time series signals to the CNN-SVM combined model for the pattern recognition of knee motion. Therefore, we proposed a CNN-SVM combined model to make use of their advantages. On the other hand, SVM has good generalization ability and can solve the small sample problem. In contrast, convolutional neural network (CNN), which has been widely applied to computer vision, can learn to automatically extract features from the training data by means of convolution and subsampling, but CNN training usually requires large sample data and has the overfitting problem. These features generally require the domain knowledge for researchers to be designed and take a long time to be tested and selected for high classification performance. The commonly used classifiers for pattern recognition of human motion, like backpropagation neural network (BPNN) and support vector machine (SVM), usually implement the classification by extracting some hand-crafted features from the human biological signals.