Correct classification rate

For qualitative methods in which more than two (J) possible outcomes or classifications per sample are possible, it is common to simply report the fraction or percentage of correct classifications (CC) for each individual class, as well as an overall correct classification rate. 

Nevertheless, in most of the electronic tongue applications found in the literature, classification techniques like linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) have been used in place of more appropriate class-modeling methods. Moreover, in the few cases in which a class-modeling technique such as soft independent modeling of class analogy (SIMCA) is applied, attention is frequently focused only on its classification performance (e.g., correct classification rate). Use of such a restricted focus considerably underutilizes the significant characteristics of the class-modeling approach. 

Beltran et al. (50) succeeded in classifying 172 Chilean wines according to the type of grapes (cabernet sauvignon, merlot, and carmenere). First, phenolic compound chromatograms were developed with FIPLC-DAD. Second, features were extracted from the chromatographic data with different feature extraction techniques, like discrete Fourier transform and Wavelet transform. Finally, next to other different classification techniques, LDA and QDA were applied. From CV, both methods were found to result in acceptable correct classification rates without statistically significant difference between both rates. 

Beltran et al. (50) also tested kNN to classify the Chilean wines according to their grape type. Again different feature extraction techniques were tested to reduce the dimensionality of the chromatographic data, describing the phenolic compounds. In most cases, kNN resulted in a slightly lower average correct classification rate than LDA and QDA. 

Fig. 4.19 Artificial neural network (ANN) and SUBSTRUCT classification of CNS activity. Result of the ANN classification of CNS activity (left) the correct classification rate is given as a function of the limit separating CNS-i- and CNS- molecules, respectively. For example, if the limit is 0.5 all molecules with a score <0.5 are classified as...

Using these parameters, a 74.8% overall correct classification rate was achieved. Jackknifed classification tests showed a 74.6% overall correct classification rate. 

The correct classification rate (CCR) or misclassification rate (MCR) are perhaps the most favoured assessment criteria in discriminant analysis. Their widespread popularity is obviously due to their ease in interpretation and implementation. Other assessment criteria are based on probability measures. Unlike correct classification rates which provide a discrete measure of assignment accuracy, probability based criteria provide a more continuous measure and reflect the degree of certainty with which assignments have been made. In this chapter we present results in terms of correct classification rates, for their ease in interpretation, but use a probability based criterion function in the construction of the filter coefficients (see Section 2.3). Whilst we speak of correct classification rates, misclassification rates (MCR == 1 - CCR) would equally suffice. The correct classification rate is typically formulated as the ratio of correctly classified objects with the total... 

Here 8 is an indicator variable such that 5(yi,yj) = 1 if yj = yj and zero otherwise. (For an interesting documentation involving error-rate estimation procedures to simulated data, the reader is referred to [2]). Eq. (6) is based on the training data, and as mentioned earlier, this result is likely to give an overly optimistic impression of the classification model. The correct classification rate for the testing data which is defined by... 

We consider a classification QSAR model predictive, if the prediction accuracy characterized by the correct classification rate (CCR) for each class is sufficiently large ... 

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