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Unsupervised user perception index importance degree determination method
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A technology for determining method and importance, applied in the field of unsupervised user-perceived indicator importance determination, which can solve problems such as excessive subjectivity, inability to obtain target values, and difficulty in obtaining valuable results.
Pending Publication Date: 2022-07-29
JILIN UNIV
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However, in many scenarios, it is impossible to obtain the target value, which requires a method to calculate and locate the importance of indicators through the internal laws of key indicator data
[0004] Taking the user's comprehensive online perception index as an example, if the traditional method is used, it is necessary to obtain a target value that can truly and objectively evaluate the user's comprehensive online perception. In reality, the closest value to this value is the user satisfaction survey score, but this value is subjective. If it is too strong and the sample size is too small, it will be difficult to obtain valuable results as the target value of index importance ranking
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Embodiment 1
[0050] refer to Figure 1-8 , an unsupervised method for determining the importance of user perception indicators, including the following steps:
[0052] Specifically, in step S1, the calculation method of the correlation coefficient matrix is as follows: using the Pearson correlation coefficient formula to calculate the correlation coefficient matrix Corr.
[0053] S2. Soft threshold method to construct an adjacency matrix;
[0054] Specifically, in step S2, the specific step is to perform weighting based on the correlation coefficient matrix Corr obtained in step S1 by a soft threshold method, and calculate to obtain an adjacency matrix.
[0055] S3. Calculate the topological overlap matrix;
[0056] Specifically, in step S3, the specific steps are to introduce a topology overlapping matrix into the result obtained in step S2;
[0057] Using python, the topological overlap matrix is calculated.
[0058] S4....
Embodiment 2
[0063] refer to Figure 1-9 , on the basis of Embodiment 1, in step S1:
[0064] The correlation coefficient matrix is specifically: a matrix obtained based on the candidate index sample set X after data preprocessing, and the specific formula is as follows:
[0065]
[0066]Among them, [X1, X2...Xn] is an indicator for each column, there are a total of n indicators, and the sample data size is m, that is, the matrix is m rows, so the size of the matrix X is m×n.
[0067] Specifically, since there are n indicators, the size of the correlation coefficient matrix is n×n, and any element in the matrix cor(Xi,Xj), i,j∈[1,n] is the interval between the i and jth indicators The Pearson correlation coefficient of , then:
[0068]
[0069] in,
[0070]
[0071] In the formula cov(X 1 ,X 2 ) is X 1 and X 2 the covariance of , for X 1 Variance, for X 1 average of.
[0072] In step S2:
[0073] The specific steps of obtaining the adjacency matrix by weightin...
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Abstract
The invention discloses an unsupervised user perception index importance degree determination method, which belongs to the field of wireless communication, and comprises the following steps: S1, calculating a correlation coefficient matrix; s2, constructing an adjacent matrix by a soft threshold method; s3, calculating a topological overlapping matrix; and S4, calculating the importance degree of the candidate indexes. The connectivity between the indexes of each dimension can be objectively calculated, and the importance of the perception indexes is represented according to the connectivity; a target value does not need to be defined, and index importance information is calculated and obtained only through an unsupervised learningalgorithm based on an internal rule of candidate index data.
Description
technical field [0001] The invention relates to the technical field of wirelesswireless communication, in particular to an unsupervised method for determining the importance of user perception indicators. Background technique [0002] Most of the existing index importance ranking methods use supervised machine learning algorithms to find the degree of influence of multi-dimensional indicators on a specific target to determine the index importance. For example, according to the correlation factor between each indicator and the target, sort each indicator in descending order to obtain an indicator ranking table; or use ordinal relationship analysis method to sort the indicators; another example, input the relevant features into the machine learning model, The machine learning model targets the gain of a certain index according to the relevant features, and sorts the importance of the relevant features according to the size of the gain value. [0003] All of the index importa...
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