J. Egan, Signal Detection Theory and ROC analysis. Series in Cognition and Perception, Academic Press, New York, 1975.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... or by proposing phantom relations between two otherwise unrelated genes (false positives) A global picture of these interactions can be seen in Figure 3, the Receiver Operator Characteristic (ROC) figure, with each curve parametrized by the cut o# threshold in the range of [ 1, 1] An ROC curve ([12]) for a given metric plots sensitivity against (1 specificity) where = TP(#) Specificity = fraction of negatives detected by a metric = TN(#) and TP(#) FN(#) FP(#) and TN(#) denote the number of True Positives, False Negatives, False Positives, and True Negatives, respectively, arising ....

Egan, J.P. Signal Detection Theory and ROC analysis.

....of class observations that are correctly classified is used as an estimate of . Likewise, the fraction of class observations that are correctly classified is used as an estimate of . A popular construct used for describing the performance of a diagnostic classifier is the ROC curve [6] 7] [20], 21] A ROC curve is generated by varying the value of one (or more) of the components of the parameter vector , and plotting the corresponding and values. For example, the Fig. 2. The two ROC curves have equal Az values, but, depending upon the relative preferences concerning the sensitivity or ....

....ROC curve is obtained when the curve is generated from an independent data set and represents an unbiased estimate of classifier performance [24] Two typical ROC curves are shown in Fig. 2. The area under a ROC curve, or , is an accepted way of comparing overall classifier performance [6] 7] [20], 21] Two curves may have equal values, as shown in Fig. 2. However, one of the curves will typically be preferred over the other, depending upon the relative preference of the sensitivity and the specificity needed for the task at hand. For certain types of classifiers, such as rule based ....

[Article contains additional citation context not shown here]

J. Egan, Signal Detection Theory and ROC Analysis. New York: Academic, 1975.

.... or by proposing phantom relations between two otherwise unrelated genes (false positives) A global picture of these interactions can be seen in Figure 3, the Receiver Operator Characteristic (ROC) figure, with each curve parametrized by the cut o# threshold in the range of [ 1, 1] An ROC curve ([13]) for a given metric plots sensitivity against (1 specificity) where Sensitivity = fraction of positives detected by a metric = TP(#) TP(#) FN(#) 10) Specificity = fraction of negatives detected by a metric = TN(#) TN(#) FP(#) 11) and TP(#) FN(#) FP(#) and TN(#) denote the ....

Egan, J.P. (1975), Signal Detection Theory and ROC analysis, Academic Press, New York.

....a 15 minute time window. If #(r) #, we say that a failure has occurred. Given #(r) we determine the probability of a false positive, PFP , and the probability of detection, PD , for various values of #. This is commonly called the receiver operating characteristic, or ROC, for a decision rule [8]. BGP based prediction achieves a 0.5 probability of detection for false positive rates of less than 0.01 for many hosts, as shown in Figure 15. Each line on this graph corresponds to failure tests between MIT and some other host; each point on a line corresponds to the threshold number of BGP ....

EGAN, J. Signal Detection Theory and ROC Analysis. Academic Press, New York, 1975.

....compare these two models with PLUMS in terms of performance and spatial accuracy(ADNP) Metric of Comparison for Classification accuracy: Classification accuracy achieved by classical and spatial logistic regression are compared on the test data. We use the Receiver Operating Chen acteristic(ROC) [8] curves to compare classification accuracy. ROC curves plot the rela tionship between the true positive rate(TPR) and the false positive rate(FPR) For each cut off probability b, TPR(b) measures the ratio of the number of sites where the nest is actually located and was predicted divided by the ....

J.P. Egan. Signal Detection Theory and ROC analysis. Academic Press, New York, 1975.

....locations on the test data. Classification accuracy, which we describe next, was used to evalute the two models. Metric of Comparison: Classification accuracy achieved by classical and spatial logistic regression are compared on the test data. We use the Receiver Operating Characteristic(ROC) [Egan, 1975] curves to compare classification accuracy. ROC curves plot the relationship between the true positive rate(TPR) and the false positive rate(FPR) For each cut off probability b, TPR(b) measures We would like to thank James Lesage(http: www.econ.utoledo.edu lesage) for making the matlab ....

Egan, J. (1975). Signal Detection Theory and ROC analysis. Academic Press, New York.

....locations on the test data. Classification accuracy, which we describe next, was used to evalute the two models. Metric of Comparison: Classification accuracy achieved by classical and spatial logistic regression are compared on the test data. We use the Receiver Operating Characteristic(ROC) [8] curves to compare classification accuracy. ROC curves plot the relationship between the true positive rate(TPR) and the false positive rate(FPR) For each cut off probability b, TPR(b) measures the ratio of the number of sites where the nest is actually located and was predicted divided by 12 ....

J.P. Egan. Signal Detection Theory and ROC analysis. Academic Press, New York, 1975.

.... of deciding that a target is present when in fact only a distractor is present (false alarm) In order to maximize the probability of a correct response, P (C) P (Y es j T = 1) P (T = 1) 1 P (Y es j T = 0) P (T = 0) 3) the following maximum likelihood decision rule must be adopted (cf. [12]) for, e.g. the unimodal visual case: If P (T = 1 j V = v) P (T = 0 j V = v) then decide Yes , otherwise decide No . The above inequality is equivalent to = 1; where the right most ratio is a function of V , L(V ) the likelihood ratio. Thus, the above rule is equivalent to: If ....

....and distractor signals. Intuitively, this sensitivity should be a (decreasing) function of the amount of overlap between the driven and the spontaneous likelihood (e.g. P (V = v j T = 1) and P (V = v j T = 0) One possible appropriate measure of sensitivity for the Poisson observer is (cf. [12]) D V = 1 0 ( 1 0 ) 1=4 and DA = 1 0 ( 1 0 ) 1=4 (4) for the visual and auditory unimodal inputs, resp. A natural choice for the bimodal measure of sensitivity then is D V A = 1 1 ) 0 0 ) 1 1 ) 0 0 ) 1=4 : 5) Note that, unlike the hit ....

Egan, J. P. (1975). Signal detection theory and ROC analysis. New York: Academic Press.

....false alarm rate ff. Changing c generated a new P V and P F . By varying c from it s minimum to maximum value, we obtained all combinations of P V and P F . A plot of all combinations of P V and P F is a receiver operating characteristic (ROC) also known as the relative operating characteristic) [1,3]. The input to the scoring algorithm was s i (k)# thresholding similarity scores, and computing P V , P F , and the ROCs was performed by the scoring algorithm. The above method computed a ROC for an individual. However, weneedperformance over a population of people. To calculate a ROC over a ....

J. P.Egan.Signal Detection Theory and ROC Analysis. Academic Press, 1975.

....using 16. Two other results are worth pointing out. First, to test whether the increase in these measures was significant, we ran multiple models with different training and verification sets. We calculated from formulas that the top 5 lift had a standard deviation of 0. 11 and the ROC metrics [20, 6] had a standard deviation of 0.0037. Second, these results were obtained using a boosted naYve bayesian classifier; a classification tree induction technique produced analogous results. 8 Related Work Data preprocessing is a standard practice in statistics [28] pattern recognition and data ....

Egan J.P. Signal Detection Theory and ROC Analysis, Series in Cognitition and Perception, Academic Press, 1975, New York, NY.

No context found.

J. Egan, Signal Detection Theory and ROC analysis. Series in Cognition and Perception, Academic Press, New York, 1975.

No context found.

James P. Egan. Signal Detection Theory and ROC Analysis. Academic Press, Inc., 2003.

No context found.

J. P. Egan. Signal Detection Theory and ROC analysis. Academic Press, New York, 1975.

No context found.

Egan, J. (1975). Signal detection theory and ROC analysis. Academic Press.

No context found.

J.P. Egan. Signal Detection Theory and ROC Analysis.NewYork:Academic Press, 1975.

No context found.

Egan, J.P., Signal detection theory and ROC analysis. Academic Press, Inc., New York, 1975

No context found.

Egan, J.P. Signal Detection Theory and ROC analysis.

No context found.

J. P. Egan. Signal Detection Theory and ROC Analysis. Series In Cognition and Perception. Academic Press, New York, 1975.

No context found.

J.P. Egan. Signal Detection Theory and ROC Analysis. Academic Press, 1975.

No context found.

Egan, J.P.: Signal Detection Theory and ROC Analysis. Series in Cognition and Perception. Academic Press, New York (1975)

No context found.

J. P. Egan. Signal Detection Theory and ROC Analysis. Series In Cognition and Perception. Academic Press, New York, 1975.

No context found.

J.P. Egan. Signal Detection Theory and ROC Analysis. New York: Academic Press, 1975.

No context found.

J. P. Egan. Signal Detection Theory and ROC Analysis. Academic Press, 1975.

No context found.

J. Egan. Signal Detection Theory and ROC analysis. Series in Cognition and Perception. Academic Press, 1975.

No context found.

J.P. Egan, Signal Detection Theory and ROC Analysis, Academic Press, New York, 1975.

First 50 documents  Next 50

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC