Ionized layer high-dimensional data feature selection method based on improved BBA algorithm

Abstract

The invention discloses an ionized layer high-dimensional data feature selection method based on an improved BBA algorithm. The method comprises the following steps: acquiring ionized layer data; taking a dimension classification loss function as a target function; adopting an improved BBA algorithm to solve the target function, wherein the improved BBA algorithm comprises the following steps: after the individual speed is updated in a single dimension, mapping from a continuous space to a discrete space is conducted on the updated individual speed according to a time-varying V-shaped conversion function; and determining a target dimension after solving, and performing dimension reduction processing on the ionized layer data according to the target dimension to obtain ionized layer features corresponding to the target dimension. A random black hole model is introduced, a time-varying V-shaped conversion function is provided to improve a BBA algorithm, after ionized layer high-dimensional data is subjected to dimension reduction based on an improved discrete binary bat algorithm, a minimized feature subset is generated, the data error rate is reduced, the dimension classification precision is improved, and accurate ionized layer data features are selected.

Description

technical field [0001] The invention relates to the technical field of high-dimensional data feature selection, in particular to an ionospheric high-dimensional data feature selection method based on an improved BBA algorithm. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Data mining is a branch derived from the rapid development of information technology. It requires algorithmic analysis from massive data to extract useful information hidden in the data. It generally includes the following processing processes: ①data preparation, ②data mining, ③result expression with explanation. There is an important preprocessing step, feature selection, whose main function is to reduce irrelevant or redundant attributes in specific data. [0004] The commonly used feature selection methods can be roughly divided into filter method, wrapper method a...

AI Technical Summary

Problems solved by technology

The current problem for this type of object is that there are usually many features describing this type of ionospheric object, that is, high-dimensionality. The challenge of feature selection is: how to generate a minimized feature subset under the condition of high-dimensional data, At the same time, it has no impact on the classification accuracy or minimizes the impact; the existing algorithms usually obtain relatively more dimensions after dimensionality reduction, which means that there is still room for dimension reduction; in addition, for this type of ionospheric objects, the existing algorithms get The error rate of the data after dimension reduction is high, and the reason for the high error rate is that the selection of dimension by the existing algorithm is not accurate enough

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