Feature selection method based on Filter selection algorithm and Wrapper selection algorithm

A feature selection method and a technology for selecting algorithms, applied in the field of machine learning, can solve problems such as low discrimination performance, low algorithm efficiency, and difficulty in completely eliminating redundant features, so as to improve algorithm efficiency and reduce computing costs

Inactive Publication Date: 2018-09-07
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0009] 1. The method of Embedded mode depends on the machine learning algorithm, and its applicability is not high
[0010] 2. The Wrapper selection algorithm is inefficient because it needs to use each candidate feature subset to train the model for evaluation
[0011] 3. The filter selection algorithm is m

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  • Feature selection method based on Filter selection algorithm and Wrapper selection algorithm
  • Feature selection method based on Filter selection algorithm and Wrapper selection algorithm
  • Feature selection method based on Filter selection algorithm and Wrapper selection algorithm

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Embodiment Construction

[0043] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0044] like figure 1 Shown is a schematic flow chart of the feature selection method based on the Filter and Wrapper selection algorithm of the present invention. A feature selection method based on Filter and Wrapper selection algorithm, comprising the following steps:

[0045] A. Import all feature subsets and set initial parameters;

[0046] B. Use the variance method to calculate the mean and variance of each feature in the data set, and eliminate the features that do not diverge;

[0047] C. Using the Pearson correlation coefficient method to calculate the Pearson correlation coefficient bet...

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Abstract

The invention discloses a feature selection method based on a Filter selection algorithm and a Wrapper selection algorithm. The method comprises the steps that all feature subsets are imported, a variance method is adopted to screen divergent features, a Pearson correlation coefficient method is adopted to screen non-redundant features, a feature space search method is adopted to generate new feature subsets, a neural network training learning model is adopted to construct an evaluation standard of the feature subsets, and the feature subsets are output. According to the method, the advantagesof the Filter selection algorithm and the advantages of the Wrapper selection algorithm are combined, and by use of the complementary characteristics of the two algorithms, calculation cost is reduced while algorithm efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a feature selection method based on a Filter and Wrapper selection algorithm. Background technique [0002] Feature selection is one of the important research topics in the fields of machine learning, pattern recognition and statistics. Feature selection refers to selecting the feature set that obtains the best performance of the corresponding model and algorithm. Usually, when we construct a machine learning algorithm, we can collect data information in many dimensions, but when the feature dimension reaches a certain level, putting all the features into the algorithm will bring a disaster of dimensionality. Difficult to achieve convergence, and may even overflow the calculation. When faced with this kind of problem, feature selection becomes very important. [0003] Generally speaking, data and features determine the upper limit of machine learning, and ...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/211G06F18/214
Inventor 廖伟智严伟军阴艳超张强曹奕翎
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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