Minimum classification error (MCE) is a measure of the quality of a classification model in artificial intelligence and machine learning. This measure refers to the lowest error rate that can be achieved when classifying data from a test set.

The MCE is used to assess the ability of a classification model to generalize to new and unseen data, known as generalizability. A model with a low MCE is able to correctly classify most test data and has better generalization ability than a model with a high MCE.

The MCE is determined by comparing the model predictions with the actual labels of the test data. Classification error is defined as the proportion of examples that are misclassified. The MCE is achieved when the minimum possible classification error value is found for the model, implying that the model is as accurate as possible in the classification task.

The MCE is an important measure in the development and evaluation of classification models in artificial intelligence and machine learning, as it allows comparing the quality of different models and selecting the best one for a specific task. In addition, the MCE can help identify areas where the model needs improvement to improve its generalization capability.

It can also be considered as a variant of the LVQ (Learning Vector Quantization) method. In this sense, MCE is a training technique that uses the minimum classification error criterion to adjust the weights of the coding vectors in the LVQ network. The goal of MCE is to minimize the classification error rate, i.e., the proportion of incorrectly classified samples. MCE uses a cost function that measures the discrepancy between the network output and the expected value of the output for each training sample.

MCE is used in binary and multiclass classification problems, and is useful when the number of training samples is limited or when the classes are unbalanced.

Reference: Biing-Hwang Juang and Shigeru Katagiri. Discriminative learning for minimum error classication..