Unbalanced data classification system based on geometric structure integration

Abstract

The invention discloses an unbalanced data classification system based on geometric structure integration, and the system comprises the following modules: an input module which converts collected samples according to the specific description of an unbalanced problem to obtain a sample set in a vector form, and the sample set in the vector form comprises a few types of samples and a plurality of types of samples; the training module is used for training the sample set in the vector form to obtain a few types of decision areas of the system; and the test module is used for inputting a to-be-discriminated sample and judging whether the to-be-discriminated sample is in the minority class decision area of the system to obtain the class to which the to-be-discriminated sample belongs. Accordingto the method, weak classifiers are designed by utilizing a supporting hyperplane principle, so that each weak classifier can identify a plurality of different types of samples, and labor division exists among the weak classifiers; through the combination of corresponding decision area spaces, the designed integration strategy can effectively identify a few types of samples and a plurality of types of samples, so that the problem of imbalance is effectively solved.

Description

technical field [0001] The invention relates to the field of data processing, in particular, an integrated classification system based on sample distribution geometric structure is designed to process sample distribution unbalanced data classification system. Background technique [0002] Today's world is ushering in a new round of technological development and transformation, and artificial intelligence will be an important force to promote its development and transformation. Pattern recognition is the study of using computers to imitate or realize the recognition ability of humans or other animals, so that the research objects can complete the task of automatic recognition. As we all know, the concept of linear or vector space widely exists in many scientific fields. When we use the concept of "space", we have established a close relationship with geometry. In the field of pattern recognition, many algorithms are also based on spatial projection mapping. When traditional...

AI Technical Summary

Problems solved by technology

[0006] In view of the lack of intuition, complex technical structure, lack of diversity and diversity among classifiers, and difficulty in determining the number of weak classifiers in the existing integrated learning technology, etc.

Images