Weight clustering and under-sampling-based unbalanced data classification method

Abstract

The classification of unbalanced data sets already becomes one of most challenging problems in data mining. A quantity of minority class samples is far smaller than a quantity of majority class samples, so that the minority classes have the defects of low accuracy, poor generalization performance and the like in a classification learning process of a conventional algorithm. The algorithm integration already becomes an important method for dealing with the problem, wherein random under-sampling-based and clustering-based integrated algorithms can effectively improve classification performance. But, the former easily causes information loss, and the latter is complex in calculation and difficult to popularize. The invention provides a weight clustering-based improved integrated classification algorithm fusing under-sampling, which is specifically a weight clustering and under-sampling-based unbalanced data classification method. According to the algorithm, a cluster is divided according to weights of the samples, a certain proportion of majority classes and all minority classes are extracted from each cluster according to weight values of the samples to form a balanced data set, and classifiers are integrated by utilizing an Adaboost algorithm framework, so that the classification effect is improved. An experimental result shows that the algorithm has the characteristics of accuracy, simplicity and high stability.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to an unbalanced data set classification method based on Adaboost algorithm weight clustering and under-sampling. Background technique [0002] With the development of Internet technology, the type and quantity of information that people obtain are increasing rapidly. A large amount of data noise and more complex data release types will bring new challenges to our data analysis. Among them, the classification of imbalanced datasets has become one of the most challenging problems in data mining, which widely exists in medical diagnosis, credit evaluation and other fields. In unbalanced data, the number of samples of the majority class is far greater than that of the minority class. If ordinary machine learning methods and evaluation criteria are used, the minority class may be ignored or even directly treated as noise. Therefore, it is often difficult for ordinary mac...

AI Technical Summary

Problems solved by technology

However, in the undersampling of each round of Adaboost iterations, the RUSBoost algorithm randomly extracts samples from the majority class, so this method will cause the loss of information of the majority class samples.

Even if the lost information is compensated to a certain extent by the Boosting method, the degree of compensation is random and undeterminable

Images