Feature selection method based on filtering type and packaging type hierarchical progression

Abstract

The invention relates to a feature selection method based on filtering type and packaging type hierarchical progression. The method comprises the following steps: firstly, sorting features by using a filter type variance sorting method, an information gain sorting method and a Boruta sorting method based on a packaging type; distributing ranks to the sorted features according to importance degrees; fusing results of the three sorting methods; and then calculating correlation between every two features based on a Pearson's correlation coefficient, so as to obtain a feature fusion result; then setting a Pearson's correlation coefficient threshold of the features, selectively deleting part of the features according to the correlation between the features, and finally finding out the best feature combination based on a packaged sequence forward selection method in combination with a random forest model, thereby obtaining an optimal feature subset. The method has a good effect of selecting the optimal feature subset for the data set, and provides relatively accurate feature information for the learning model, so that the accuracy of the learning model is improved.

Description

Technical field: [0001] The invention relates to a feature selection method based on filtering and encapsulation, which has a good application in the feature selection of data sets. Background technique: [0002] There are a large number of redundant features and irrelevant features in the data set, which brings great challenges to data mining and seriously affects the accuracy and scientificity of data mining results. Therefore, before data mining, irrelevant features and irrelevant features in the data set Redundant features are processed. [0003] Feature selection is also called feature subset selection. Feature selection can propose irrelevant or redundant features from the original features, reduce the number of features to find the optimal feature subset, improve model accuracy, and reduce running time. The feature selection method can be divided into filtering type and encapsulation type according to whether it is independent of the subsequent learning algorithm. Th...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem of irrelevant or redundant features of data sets, the present invention discloses a feature selection method based on filtering and encapsulation.

Images