confusion matrix


Also found in: Wikipedia.

confusion matrix

[kən′fyü·zhən ‚mā·triks]
(computer science)
In pattern recognition, a matrix used to represent errors in assigning classes to observed patterns in which the ij th element represents the number of samples from class i which were classified as class j.
References in periodicals archive ?
The confusion matrix in Figure 6 shows that the "anger" is mixed with the "happiness" and vice versa.
As seen in the test confusion matrix in Figure 2 the tested system was able to correctly classify more than 97% of the images.
Various model performance metrics can be derived from the confusion matrix, given above.
In the field of artificial intelligence a confusion matrix is commonly used to evaluate the system performance.
The performance of the FMMIS method on the test set of pine board images is measured using the following criteria: confusion matrix, area recognition rate (ARR), average processing time, and segmentation quality.
Detected blocks are compared with the original clip and the confusion matrix is formed as described in Table 1.
Confusion Matrix is a binary classification model classifies each instance into one of two classes: a true and a false class.
The accuracy of the classifications are then assessed by comparing the classifications with a reference set using a confusion matrix.
The confusion matrix is connected to a data table widget with which the user can observe particular kinds of misclassifications.
The Classifier Output panel from the GUI Classify implicitly displays the run information (the number of instances, number of attributes, the testing manner of the model), the classifier model, the test result (Correctly Classified Instances, Incorrectly Classified Instances, Kappa statistic, Mean absolute error, Root mean squared error, Relative absolute error, Root relative squared error, Total Number of Instances), Detailed Accuracy By Class (True Positive Rate, False Positive Rate, Precision, Recall, FMeasure, Class) and Confusion Matrix (Tan et Al.
The voiceless CV syllables LA-experience confusion matrix shows decreased /h/ and increased /f/ responses (Figure 5(d)).
6 shows the confusion matrix for the proposed approach on the new DT-10 dataset corresponding to the recognition rate of 89.