A feature screening method and device
A feature screening and feature set technology, applied in the field of data processing, can solve problems such as inability to apply dynamic changes of feature data sets
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Embodiment 1
[0102] Such as figure 1 As shown, the feature screening method provided by the embodiment of the present invention may include the following steps:
[0103] Step 101: Obtain at least two features to be screened;
[0104] Step 102: respectively determine the evaluation metric value corresponding to each feature;
[0105] Step 103: According to the correlation between each feature, divide at least two features into at least two feature subsets, wherein each feature subset includes at least one feature, and the corresponding evaluation metric value in each feature subset is the largest The correlation coefficient between one feature and other features is greater than the preset first correlation coefficient threshold;
[0106] Step 104: Obtain a feature with the largest corresponding evaluation metric value from each feature subset, and construct each acquired feature into an optimal feature set;
[0107] Step 105: Update the features included in the optimal feature set by cal...
Embodiment 2
[0111] On the basis of the feature screening method provided in Embodiment 1, the process of determining the evaluation metric value corresponding to each feature in step 102 can be specifically implemented through the following steps:
[0112] A1: Construct the first feature set including the acquired features, and obtain the classification set of the first feature set in the pre-obtained decision feature subset, where the decision feature subset is the sum of the pre-obtained decision feature set Subset;
[0113] Substitute the decision feature subset, classification set, and decision feature set into the following equation set 1 to obtain the first evaluation metric parameters corresponding to each feature, where equation set 1 includes:
[0114]
[0115] Among them, U represents the first feature set, and U={x 1 ,x 2 ,x 3 …x n}, n represents the total number of features; B represents the subset of decision-making features, D|B represents the classification set, and ...
Embodiment 3
[0124] On the basis of the feature screening method provided in Embodiment 2, the process of dividing each feature into at least two feature subsets in step 103, such as figure 2 As shown, it can be realized through the following steps:
[0125] Step 201: Substituting the samples included in each sample set into Equation 3 to obtain a set of correlation parameters of each feature relative to other features;
[0126] Equation set three includes:
[0127]
[0128] in, Characterizes the correlation parameter of the i-th feature with respect to the j-th feature; X j Represent a set consisting of samples corresponding to the jth feature in each sample set; F i Represent a set consisting of samples corresponding to the i-th feature in each sample set; W i Characterize the correlation parameter set corresponding to the i-th feature;
[0129] Step 202: For each feature pair including two features, calculate the mean value of the correlation parameter of the first feature rel...
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