Evaluation method for performance influence degree of classification models by class imbalance

Abstract

The invention relates to an evaluation method for performance influence degree of classification models by class imbalance. The evaluation method comprises the following steps of (1) building a classification model base; (2) constructing a new data set; (3) forecasting the new data set by the classification models; (4) evaluating the performance of the classification models; and (5) evaluating an influence degree level. According to the evaluation method, firstly, a typical classification algorithm in machine learning is adopted to build the classification model base; secondly, a class imbalance data set is selected as a reference data set, a group of new data sets with imbalance ratio gradually increased is built on the basis, different classification models are selected to respectively classify and forecast the group of new data sets; and finally, a variable coefficient is adopted to evaluate the performance variation degree of the classification models and also carry out level division, thus, the influence degree of the class imbalance on the performance of different classification models is evaluated, and a guidance significance is played in research on the class imbalance process. With regards to different classification models, the evaluation method for performance influence degree of the classification models by class imbalance, provided by the invention, has high universality.

Description

technical field [0001] The invention belongs to the fields of data mining and machine learning, and relates to an evaluation method of a classification model, in particular to an evaluation method of the influence degree of class imbalance on the performance of the classification model. Background technique [0002] Classification is an important technology in the field of data mining. It refers to the process of building a classification model through learning on the data of known categories, and then predicting the data of other unknown categories. In the process of building a classification model, it is often necessary to combine algorithms or models in machine learning to improve the accuracy of classification. [0003] With the continuous development of data mining and machine learning, the problem of class imbalance has gradually become a research hotspot in these fields. In general, class imbalance refers to the imbalance in the distribution of sample sizes among dif...

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Problems solved by technology

However, while solving the classification imbalance problem, it often needs to be combined with a specific classification model or verified under certain classification models. The above methods do not fully consider the impact of the class imbalance problem on the classification model itself.

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