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Feature selection method based on covariance metric factor

A feature selection method and measurement factor technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of reducing the classification performance of the classifier, too large space dimension, etc., and achieve the effect of reliable feature selection algorithm.

Pending Publication Date: 2021-12-14
XIAN UNIV OF TECH
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Problems solved by technology

If the feature space dimension is too large, the classification performance of the classifier will be reduced, and the Hughes phenomenon will appear.

Method used

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  • Feature selection method based on covariance metric factor
  • Feature selection method based on covariance metric factor
  • Feature selection method based on covariance metric factor

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Embodiment Construction

[0047] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0048] The present invention provides a feature selection method based on covariance measurement factors, such as figure 1 As shown, the specific steps are as follows:

[0049] Step 1. Select different text type data sets, and perform preprocessing operations, that is, perform word segmentation operations, and remove stop words in the text. The text data is represented by the vector space model, and the feature words that appear in the data are more than 25% of the total number of documents or less than 3 are removed. The data set is divided according to the ratio of 9:1, that is, 90% of the samples in the data set are randomly selected as the training set data, and the remaining 10% of the samples are used as the test set data.

[0050] Step 2. Set the size of the optimal feature subset to C, use the feature sorting function to calculate t...

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Abstract

According to a feature selection method based on a covariance measurement factor, on the basis of an original triangular comparison measurement algorithm (TCM), the concept of the covariance measurement factor is introduced, and the correlation between features and categories is further measured on the document frequency level by calculating covariance values of feature words and the categories. When the performance of the method is verified, a naive Bayes algorithm is used for classification operation, and a macro F1 and a micro F1 are used for evaluating the classification effect. According to the method, feature words highly related to the categories can be better screened out, the method is a reliable feature selection algorithm, and the classification accuracy and efficiency are improved.

Description

technical field [0001] The invention belongs to the technical field of text classification methods, and in particular relates to a feature selection method based on a covariance measurement factor. Background technique [0002] With the wide application of big data technology, a large number of unstructured text information emerges on the World Wide Web and is stored and processed by computers, such as user comments on music and video software; user feedback and purchase records on e-commerce platforms; Articles, comments, etc. To process huge unstructured text data, technologies such as data mining and natural language processing must be used. Among them, text classification is widely used. Text data is divided into different categories through model learning, which facilitates further data processing. Text type data often consists of tens of thousands of feature words, which contain a large number of irrelevant and redundant features, which have a negative impact on class...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06F40/216G06F40/289G06K9/62
CPCG06F16/35G06F40/216G06F40/289G06F18/2113G06F18/213G06F18/24155
Inventor 周红芳李想王晨光连延彬
Owner XIAN UNIV OF TECH
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