Breast cancer data classification method based on single category

Abstract

The invention discloses a breast cancer data classification method based on a single category. According to the breast cancer data classification method, breast cancer data classification is effectively carried out only by utilizing single-class sample data and unlabeled sample data; a probability graph model is adopted to describe the relation among the data characteristics, the observation tagsand the real labels, and a corresponding joint probability distribution model is given, and then an EM algorithm is used for solving the model, and then test data are classified. The breast cancer data set used in the breast cancer data classification method is only composed of a single-class sample and an unlabeled sample, so that the sample labeling cost is reduced, and the problem that negative-class sample data are lacked in breast cancer diagnosis is solved.

Description

technical field [0001] The invention belongs to pattern recognition technology, in particular to a single-category-based breast cancer data classification method. Background technique [0002] Existing breast cancer data classification methods are often based on supervised learning methods, which cannot fully meet the reality of breast cancer diagnosis. More and more scholars at home and abroad are paying attention to the research and application of intelligent diagnosis of breast cancer, and have done a lot of research in this area. The main methods in recent papers are: [0003] Olvi L.Mangasarian and others took the lead in using linear programming for breast cancer diagnosis in 1995; K.Polat et al. used the least squares support vector machine to maximize the classification interval to classify breast cancer data in 2007; Ayman M.Eldeib et al. People in 2016 employing deep belief networks for breast cancer data classification. [0004] In terms of patents, the Chinese...

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Problems solved by technology

[0005] Although the above methods can obtain better diagnostic and classification results, breast cancer data in real life have problems such as difficulty in collecting negative samples and expensive collection costs. The above methods are difficult to solve such problems and all include observation labels that do not depend on data strong assumption

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