Research on unbalanced class learning method based on logistic regression mixed strategy

Yucai Zhou

doi:10.1117/12.2670337

1 June 2023 Research on unbalanced class learning method based on logistic regression mixed strategy

Yucai Zhou

Proceedings Volume 12625, International Conference on Mathematics, Modeling, and Computer Science (MMCS2022); 1262504 (2023) https://doi.org/10.1117/12.2670337
Event: International Conference on Mathematics, Modeling and Computer Science (MMCS2022),, 2022, Wuhan, China

Abstract

In the context of the era of big data, machine learning and pattern research are the main contents of the technical discussion of scholars around the world. Faced with the continuous increase of data information, the problem of class imbalance appears in the relevant technical research. The main feature is that the number of instances of some classes is obviously less than that of other classes. From the Angle of practical application, in cases of hospital diagnosis, for example, because only a handful of cancer patients, so how to correctly identify all kinds of mass data information in cancer patients, practice can improve work efficiency, and can quickly find conform to the requirements of the case, to modern medical diagnosis technology research is of great significance. Therefore, on the basis of understanding the status quo of modern technology research and development, this paper, according to the relevant theories of unbalanced data set and logistic regression model, deeply discusses the unbalanced class learning method with logistic regression mixed strategy as the core. The final experimental results show that the new logistic regression algorithm can effectively improve its performance in class imbalance on the basis of guaranteeing high accuracy. Compared with other advanced methods, the logistic regression model has obvious technical advantages.

Citation Download Citation

Yucai Zhou "Research on unbalanced class learning method based on logistic regression mixed strategy", Proc. SPIE 12625, International Conference on Mathematics, Modeling, and Computer Science (MMCS2022), 1262504 (1 June 2023); https://doi.org/10.1117/12.2670337

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