Unbalanced data classification method based on boundary upsampling

Abstract

The invention relates to an unbalanced data classification method based on boundary upsampling. The unbalanced data classification method includes that Euclidean distance is used as the distance metric for each positive sample in the unbalanced dataset, K sample points nearest to the positive samples in the unbalanced dataset are taken, the class labels of the samples are compared, the number ki of negative samples in the K sample points is given, if ki >=K / 2, the positive sample point is near to the real decision boundary of positive and negative samples, conversely, the positive sample point is far from the boundary, one positive sample is randomly selected from the K nearest positive samples, and a new positive sample is generated between the selected positive sample and the positive sample selected from the K nearest positive samples, this process is subjected to iterative execution for many times until the positive and negative samples are same in number, and thus a balanced dataset is constructed, and the generated balanced dataset is trained by a Bagging algorithm to obtain a final classification model. According to the invention, a better classification effect can be achieved in the unbalanced dataset.

Description

technical field [0001] The invention relates to pattern recognition technology, in particular to a classifier for unbalanced data sets. Background technique [0002] With the development of society and the advancement of science and technology, computer automatic classification based on machine learning and pattern recognition technology is playing an increasingly important role in people's daily life. In this case, establishing an appropriate data classification model and setting credible performance evaluation standards has become a major research hotspot at present. [0003] However, the current mainstream classifiers such as support vector machines, decision trees, and extreme learning machines are designed with the overall misclassification rate as the main indicator of the training data model, and the effective premise of this method is based on the The various types of data in the sample are basically balanced (that is, the number of each type is roughly equal). But...

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Problems solved by technology

However, individual algorithm-level methods cannot process the data, and their performance is thus limited

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