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A multi-classifier fusion method based on PCA dimension reduction

A multi-classifier fusion and principal component technology, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problem of multi-dimensional feature space of user mouse behavior, achieve shortened modeling time, good experimental effect, The effect of high accuracy

Active Publication Date: 2019-03-08
XIAN UNIV OF TECH
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Problems solved by technology

[0006] The purpose of the present invention is to provide a multi-classifier fusion method based on PCA dimensionality reduction, which solves the problem that the user's mouse behavior feature space has too many dimensions during the identity authentication based on the user's mouse behavior that exists in the prior art

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  • A multi-classifier fusion method based on PCA dimension reduction
  • A multi-classifier fusion method based on PCA dimension reduction
  • A multi-classifier fusion method based on PCA dimension reduction

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[0032] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0033] A kind of multi-classifier fusion method based on PCA dimensionality reduction of the present invention, such as figure 1 , figure 2 Shown, specifically follow the steps below:

[0034] Step 1, preprocessing mouse behavior data, including data cleaning and data transformation;

[0035] Step 2. Analyze the characteristics of the overall mouse behavior and trajectory behavior, and use the PCA method to reduce the dimensionality of the features to construct new independent features. Among them, the orthogonal transformation of the original 75-dimensional features constructed by the overall mouse behavior and trajectory behavior is used to select the cumulative contribution. The principal component with a rate of 85% is used as a new mouse behavior feature, that is, a 26-dimensional new feature is selected to replace the original 75-dim...

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Abstract

The invention discloses a multi-classifier fusion method based on PCA dimension reduction. A feature selection method is applied to the data feature set, involving (step-forward selection method and)a principal component analysis method, and the method constructs a few new features to replace the original features to model, and the stacking algorithm is also applied to the multi-classifier to obtain the optimal classification effect at present in the identity authentication experiment of the user mouse behavior. The multi-classifier fusion method solves the problem that the dimension of the characteristic space of the user mouse behavior is too much when the identity authentication based on the user mouse behavior exists in the prior art.

Description

technical field [0001] The invention belongs to the technical field of multi-classifier fusion, in particular to a multi-classifier fusion method based on PCA dimensionality reduction. Background technique [0002] Identity authentication is an important guarantee for information system security, but traditional identity authentication methods have defects such as easy disclosure and loss of authentication factors, so identity authentication based on user biometrics has gradually become a hot spot in the field of identity authentication research. When exploring and researching the identity authentication method of the user's mouse behavior characteristics, in order to improve the identity authentication performance and avoid the problems of over-fitting of a single classifier and insufficient classification accuracy, a multi-classifier based on PCA dimensionality reduction was invented. The fusion method performs dynamic continuous identity authentication tasks based on user...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2135G06F18/214G06F18/254
Inventor 姚全珠赵佳瑜费蓉颜丽菁
Owner XIAN UNIV OF TECH
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