A Feature Selection Method for Elderly Balance Ability Based on CNN and RF

Abstract

The invention relates to a high-dimensional multi-granularity feature selection method based on CNN and RF, and belongs to the technical field of information processing. The present invention is based on a high-latitude multi-granularity feature data set, combined with a deep learning algorithm and a machine learning algorithm to solve the problem of high-latitude multi-granularity feature extraction. Firstly, a FSelCNN model is constructed by using the deep learning algorithm CNN model, through which the original data is converted from multi-granularity to single granularity, so that the data becomes the data required by the machine learning algorithm; finally, the machine learning algorithm RF is used from the high We can select effective features that affect practical problems from the data of weft. The present invention starts from the single feature level of high-latitude multi-granularity feature data, and converts it from multi-granularity dimension to single granularity dimension, which effectively solves the computational complexity; the model reduces the amount of parameters and can be trained in a short period of time; applicable For a variety of high-latitude and multi-granularity data, it has strong self-adaptability and has good results.

Description

technical field [0001] The invention belongs to the technical field of information processing, and relates to a method for selecting features of balance ability of the elderly based on CNN and RF high-dimensional and multi-granularity. Background technique [0002] With the explosive growth of data in the Internet era, various forms of data characteristics have emerged, and an efficient method is urgently needed to solve the problems caused by various forms of data, so as to better provide efficient data support for machine learning models , and effectively reflect the actual effect brought by the data. Moreover, feature engineering plays an irreplaceable role in the actual application of machine learning. In the field of machine learning, it is generally believed that the upper bound of machine learning algorithms depends on data and feature engineering, and the final model is just to continuously approach this upper bound through linear and nonlinear methods. [0003] Th...

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Problems solved by technology

[0004] However, although the above algorithms solve the dimensionality disaster problem in terms of dimensions, they ultimately combine features through linear combinations of some features and extract the most influential features. They are not specific to a certain feature, which does not satisfy some Specific problems, such as extracting 25 parts from 42 parts of the human body, and balancing the body balance ability of the elderly through these 25 parts

For this type of problem, traditional PCA and LDA methods can no longer meet its needs

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