A multi-classification method based on adaptive balanced integration and dynamic hierarchical decision-making

Abstract

The embodiment of the invention provides a multi-classification method based on adaptive balance integration and dynamic hierarchical decision, which includes converting the original data set into a plurality of second-class data sets according to one-to-many decomposition strategy, taking the number of the majority class samples and the minority class samples in each second-class data set as theupper and lower limits of the parameter interval respectively, taking the average accuracy rate of each class as the scoring standard, and obtaining the sampling number of each subset by grid searching method; Based on this, the over-sampling and under-sampling techniques are combined to balance the two kinds of data sets to establish a plurality of binary classification sub-models, and the binaryclassification model is obtained by integrating the sub-models through the averaging method. According to the output results of all the binary classification models, the spatial position informationof the test samples is obtained under the one-to-many framework, and the classification strategies for the blank area, the intersecting area and the normal area are established to determine the finalcategory of the test samples. The technical proposal provided by the embodiment of the invention can improve the overall recognition rate of the classification model for each category under the one-to-many framework.

Description

【Technical field】 [0001] The invention relates to a multi-classification method in the field of machine learning, in particular to a multi-classification method based on adaptive balance integration and dynamic hierarchical decision-making. 【Background technique】 [0002] When using machine learning methods to solve multi-classification problems, it is an effective means to convert the original multi-classification problem into multiple binary classification problems. Among them, the one-to-many decomposition strategy is a mainstream decomposition method, but under this framework, there are serious problems such as the imbalance in the number of positive and negative samples, and the prediction results are overly dependent on the confidence of the binary classifier. It is one of the current research hotspots to solve the class imbalance and result aggregation problem under the one-to-many framework according to the appropriate machine learning method to improve the accuracy ...

AI Technical Summary

Problems solved by technology

However, the output order given by the Naive Bayesian classifier has a certain impact on the final prediction result, and the impact is greater when the probability of multiple positive classes appearing in the output results of all two classifiers is greater than the threshold

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