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
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[0032] The present invention will be described in detail below with reference to the drawings and specific embodiments.
[0033] The present invention is a multi-classifier fusion method based on PCA dimensionality reduction, such as figure 1 , figure 2 As shown, the specific implementation is as follows:
[0034] Step 1. Preprocessing mouse behavior data, including data cleaning and data transformation;
[0035] Step 2. Analyze the overall behavior and trajectory behavior of the mouse, and use the PCA method to reduce the dimensionality of the features to construct new and independent features. Among them, the overall behavior and trajectory behavior of the mouse are orthogonally transformed from the original 75-dimensional features, and the cumulative contribution is selected The principal component with a rate of 85% is used as the new mouse behavior feature, that is, the 26-dimensional new feature is selected to replace the original 75-dimensional feature, and the specific imple...
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