Integrated transfer learning method for classification of unbalance samples

Abstract

The invention relates to an integrated transfer learning method for classification of unbalance samples, which comprises the following steps of: in the initializing process, giving different weights to positive and negative samples to ensure that the negative samples which account a small ratio for the total samples and have a large amount of information have large initial weights; in the training process in each round, extracting part of samples according to a certain ratio and using the selected samples as a training subset to carry out training, and after finishing the training, selecting the classifier with the smallest error from a plurality of simple classifiers as a weak classifier and regulating the training dataset according to a redundant data dynamic eliminating algorithm; and obtaining a weak classifier sequence after T rounds of iteration and overlaying and combining a plurality of weak classifiers into a strong classifier. According to the invention, the classification law of novel data which is distributed similarly with old data is found by effectively utilizing the classification law of the old data; particularly, a novel method is provided for solving the problem of classification of the data which is classified in an unbalance mode; the effect of a small amount of the negative samples in the classification process in the classification training process is ensured; the contribution rate of the negative samples is effectively improved; and the classification efficiency and accuracy are improved.

Description

technical field [0001] The invention belongs to the field of machine learning. Aiming at auxiliary training data with a large amount of redundant data and unbalanced positive and negative samples, an improved integrated transfer learning algorithm is proposed, and the transfer of these auxiliary training data is used to help target data to be classified. Background technique [0002] Migration learning is a hot topic in the field of machine learning in recent years. It aims at the small amount of labeled data in new tasks, and proposes to effectively use outdated data migration to new tasks: There are differences, but there will certainly be some data that will help new classification problems. In order to be able to find these useful data, a small amount of new data that has been classified is used to mine valuable information in old data. Finally, a more efficient classification model is trained based on all the useful information in the two parts of the data to realize k...

AI Technical Summary

Problems solved by technology

However, in the real world, the distribution of samples representing two different classes can be extremely unbalanced, and there are also large differences in importance

[0008] In addition, there are often a large amount of redundant data in the auxiliary data, which may be very different from the target data set. Their existence will not only affect the training speed of the model, but also lead to a decline in classification accuracy.

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