Total classification error

Analogously, the total classification error and the mean classification error for the test set eire defined for discrete cases as ... 

In the discrete case total and mean classification error for LOOCV are given by... 

As there are few data on the ratio of thoron concentration to radon, we have assumed that the value is constant and is equal to 0.14, Error(4) and Error(5) are assumed to be negligible for the error calculation. The error due to ambiguities in classification and discrimination of tracks according to the shape or the size was assessed by experiment and the relative error (1 S.D.) was less than 10%. The relative error due to the variations of F, f, and (V a) is less than 13% as described in the previous section. The combined error due to these factors is estimated to be 16%. The total error can be obtained by combining this error and Error(l). If we use the definition that the lowest detectable limit is the radon concentration at 50 % relative S.D., the lowest detectable... 

The simplest application is that of a one-way classification with equal numbers, such that a total number of N = nk observations are classified into k classes of n observations each. As an illustration, consider n standardizations of a single solution by k analysts. If, in a set of observations, / =, 2,...,k, and if 7 = 1, 2,..., an observation Xij is then the yth observation of the ith class. There is a single assignable source of variation (the different analysts) in addition to the random error inherent in the method, and evaluation of the magnitude of the variance from this source is desired. 

The simple multiscale approach can be seen as a subset of the optimal scale combination (OSC) method. In OSC the total number of possible scale combinations is generated. In the simple multiscale approach, scales were increased in a systematic fashion 0, 0 1, 0 1 2, 0 1 2 3, ... In OSC all possible combinations of the J + 1 scales are generated and tested. The combination that gives rise to a regression or classification model with the lowest number of coefficients and the lowest prediction error will be selected. Assume i is the number of scales to be selected from a total of K scales. There... 

We are perhaps now in a position to attempt to consider the matter of structural safety in its total context. We have looked at both structural reliability theory in dealing with parameter uncertainty, and its inadequacies in dealing with system uncertainty. In the previous section human error was discussed in relation to structural safety. With these considerations in mind the author has presented a classification of failure types which will be listed again here [96]. The basic types proposed are as follows ... 

In all figures, the solutions marked with a black square indicate neural networks reaching a full accuracy of 100%. We should note that, while validation and test error are calculated as Mean Squared Error (MSE) between the expected classification value (0/1) and actual neural network output (ranging in [0,1], and depending on the output activation function), the accuracy is calculated based on the confusion matrix (True Positives- -True Negatives)/(Total sample size). 

These figures show that the highest level of bus accidents as a percentage of total road accidents was in Tanzania, and the highest level of fatalities per bus accident was in Nepal. Three main classifications of causes for bus accidents in Tanzania are shown in Figure 10.3 [20,28]. These classifications are human factors, vehicle condition, and external factors. The percentage breakdowns for these three classifications were 76%, 17%, and 7%, respectively. Clearly, the main cause of bus accidents in Tanzania was human error. 

---