Classification method for imbalance problem based on cost local generalization error

Abstract

The invention discloses a classification method for an imbalance problem based on a cost local generalization error. The classification method comprises the following steps of performing normalizationprocessing on each-dimensional feature of data; according to the statistical law of the data set, calculating the misclassification cost of different types of samples; constructing a cost-sensitive multilayer perceptron neural network model; for each sample, calculating random sensitivity (ST-SM) corresponding to a local generalization error of the sample; and completing model training by combining cost sensitivity and random sensitivity of a local generalization error. According to the method, the disadvantage of excessive influence of a data level method on data set distribution is avoided,the advantage of improving the generalization ability of a local generalization error and the advantage of faster training and testing time of an algorithm-level-based method are effectively combined, so that the classification stability of the unbalanced data set and the accuracy of a classification result of each category are improved, and a more reasonable classification decision boundary is obtained.

Description

technical field [0001] The invention relates to the field of unbalanced classification of data sets, in particular to a classification method for unbalanced problems based on cost local generalization errors. Background technique [0002] With the development of computer science and technology, machine learning has played an extremely important role in many fields. In recent years, the situation of unbalanced datasets cannot be ignored in most fields and has become a hindrance to the development of machine learning. For example, in a physical examination, the number of healthy people is generally greater than the actual number of patients. If a healthy person is misclassified as a sick person, it only needs to be tested again manually; however, if the disease of the patient is ignored, the patient will not receive timely treatment. Ordinary machine learning assumes that the data set distribution is balanced, and the loss of misclassification is the same. Therefore, the mo...

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Problems solved by technology

However, the backpropagation algorithm has a disadvantage. The sequence of data input has a great impact on the results of model training. Compared with the data input later, the data input first has less impact on the model.

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