confusion matrix


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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 ?
Confusion Matrix is a binary classification model classifies each instance into one of two classes: a true and a false class.
Figure 4 shows the confusion matrix of the SVM for the top ranked 300 features.
The confusion matrix stems from whether the system is confusing two classes.
The evaluation to determine whether an original FWC land cover classification required correction relied on a confusion matrix which contained all possible combinations of the two input land cover rasters from 1987 and 2003, plus a third "tie breaker" layer developed from near-contemporaneous land use data prepared by Florida's five Water Management Districts (WMDs) and made available on Florida's statewide GIS data depository (FGDL 2012).
A mean confusion matrix was also calculated using classifications from all subjects.
As seen in the test confusion matrix in Figure 2 the tested system was able to correctly classify more than 97% of the images.
Following testing of the system by soil scientists the effectiveness of the system and the quality of the soil map produced in the system are assessed based on the metrics of map comparison, a binary logic confusion matrix, and qualitative responses from the soil scientists.
In the field of artificial intelligence a confusion matrix is commonly used to evaluate the system performance.
Confusion matrix for Order Icvcl classifications: CT, radiometric prediction Predicted Observed classification classification AN CA CH DE FE HY KA AN 0 0 1 1 0 0 0 CA 0 0 0 0 0 0 0 CH 0 0 136 25 1 0 19 DE 0 1 42 83 2 0 7 FE 0 0 2 0 4 0 2 HY 0 0 0 0 0 0 0 KA 0 1 9 10 0 0 52 KU 0 0 7 3 0 0 0 OR 1 2 4 5 0 0 5 RU 0 0 17 10 2 0 7 SO 0 0 23 20 3 0 6 TE 0 0 20 15 0 0 12 VE 1 5 126 87 2 6 26 Predicted Observed classification classification KU OR RU SO TE VE AN 0 0 0 0 0 0 CA 0 0 0 0 0 0 CH 3 4 6 21 20 108 DE 4 3 10 19 16 58 FE 0 0 0 0 0 1 HY 0 0 0 0 0 2 KA 0 4 13 17 4 18 KU 24 0 1 5 1 9 OR 2 40 3 11 4 31 RU 1 10 66 13 18 22 SO 4 5 10 96 14 53 TE 9 7 13 23 81 54 VE 7 26 27 52 38 958 Table 6.
In the confusion matrix, Good, IRF, ORF, IORF mean good bearing, bearings with inner race fault, bearings with outer race fault and bearings with both inner race and outer race faults respectively.
We used the confusion matrix to serve as similarity measure between languages, using the statistical package XLSTAT (XLSTAT, 2007).