A detection error tradeoff (DET) graph is a graphical plot of error rates for binary classification systems, plotting the false rejection rate vs. false acceptance rate.[1] The x- and y-axes are scaled non-linearly by their standard normal deviates (or just by logarithmic transformation), yielding tradeoff curves that are more linear than ROC curves, and use most of the image area to highlight the differences of importance in the critical operating region.
Two hypothetical classifiers compared via DET curves.The same two classifiers compared via traditional ROC curves.
Axis warpingedit
The normal deviate mapping (or normal quantile function, or inverse normal cumulative distribution) is given by the probit function, so that the horizontal axis is x = probit(Pfa) and the vertical is y = probit(Pfr), where Pfa and Pfr are the false-accept and false-reject rates.
The probit mapping maps probabilities from the unit interval [0,1], to the extended real line [−∞, +∞]. Since this makes the axes infinitely long, one has to confine the plot to some finite rectangle of interest.
See alsoedit
Wikimedia Commons has media related to Receiver operating characteristic.
^A. Martin, A., G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki. "", Proc. Eurospeech '97, Rhodes, Greece, September 1997, Vol. 4, pp. 1895-1898.
January 01, 1970
detection, error, tradeoff, detection, error, tradeoff, graph, graphical, plot, error, rates, binary, classification, systems, plotting, false, rejection, rate, false, acceptance, rate, axes, scaled, linearly, their, standard, normal, deviates, just, logarithm. A detection error tradeoff DET graph is a graphical plot of error rates for binary classification systems plotting the false rejection rate vs false acceptance rate 1 The x and y axes are scaled non linearly by their standard normal deviates or just by logarithmic transformation yielding tradeoff curves that are more linear than ROC curves and use most of the image area to highlight the differences of importance in the critical operating region Two hypothetical classifiers compared via DET curves The same two classifiers compared via traditional ROC curves Axis warping editThe normal deviate mapping or normal quantile function or inverse normal cumulative distribution is given by the probit function so that the horizontal axis is x probit Pfa and the vertical is y probit Pfr where Pfa and Pfr are the false accept and false reject rates The probit mapping maps probabilities from the unit interval 0 1 to the extended real line Since this makes the axes infinitely long one has to confine the plot to some finite rectangle of interest See also edit nbsp Wikimedia Commons has media related to Receiver operating characteristic Constant false alarm rate Detection theory False alarm Receiver operating characteristicReferences edit A Martin A G Doddington T Kamm M Ordowski and M Przybocki The DET Curve in Assessment of Detection Task Performance Proc Eurospeech 97 Rhodes Greece September 1997 Vol 4 pp 1895 1898 Retrieved from https en wikipedia org w index php title Detection error tradeoff amp oldid 1090994614, wikipedia, wiki, book, books, library,
