Dynamic feature selection method based on conditional mutual information

A technology of conditional mutual information and dynamic features, applied in computer parts, instruments, characters and pattern recognition, etc., can solve the problems of low classification accuracy and low efficiency, and achieve the effect of accurate redundant parts and improved classification accuracy.

Pending Publication Date: 2020-06-12
XIAN UNIV OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to provide a dynamic feature selection method based on conditional mutual information to solve the problems of low classification accuracy and low efficiency of feature selection methods in the prior art

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  • Dynamic feature selection method based on conditional mutual information
  • Dynamic feature selection method based on conditional mutual information
  • Dynamic feature selection method based on conditional mutual information

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Embodiment Construction

[0039] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0040] Relevant definitions among the present invention are as follows:

[0041] Definition 1 (Entropy) Entropy is a measure of the uncertainty of random variables, which can also be called the degree of chaos of random variables, defined as follows:

[0042]

[0043] Among them, X represents a random variable, x is a possible value of X, p(x) represents the probability distribution of x; H(X) represents the degree of chaos of the random variable X, the greater the probability of an event, or the more uneven the distribution , the smaller the entropy, the smaller the amount of information.

[0044] Definition 2 (Conditional Entropy) Conditional entropy is a measure of the uncertainty of another variable given the condition of one variable. The definition of conditional entropy is as follows:

[0045]

[0046] Among them, p(y|x) repres...

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Abstract

The invention discloses a dynamic feature selection method based on conditional mutual information, and the method specifically comprises the following steps: 1, carrying out the preprocessing of a data set, and obtaining a preprocessed data set; step 2, discretization processing is performed on the preprocessed data set, and all features in the preprocessed data set are divided into different feature levels; 3, calculating the importance degree between all the features X and the class variable Y in the data set subjected to discretization processing in the step 2; and step 4, according to theimportance I (X, Y) between the features and the classes calculated in the step 3, selecting the feature with the maximum importance as an important feature, deleting the important feature from the original feature set, adding the important feature into the candidate feature set to serve as a first candidate feature selected into the candidate feature set, and then calculating other candidate features. According to the invention, by improving the direct correlation between the features and the classes, the redundancy between the features is reduced, so that the accuracy and efficiency of feature selection are improved.

Description

technical field [0001] The invention belongs to the technical field of data mining methods, and relates to a dynamic feature selection method based on conditional mutual information. Background technique [0002] With the rapid development of information science and computer technology, especially the application of multi-sensors, the amount of information data that can be obtained is increasing, and the feature dimension is also increasing. The increase of data capacity provides conditions for data mining, but at the same time, the increase of data dimension will prolong the establishment time of the model and reduce the predictive ability of the model. Therefore, massive data also puts forward higher requirements for the design of classifiers. The feature set of these data contains a large number of redundant features and noise. Therefore, it is very important to effectively remove irrelevant features, streamline data, and remove complex noise in data to improve the abilit...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/285G06F18/211Y02D10/00
Inventor 周红芳温婧
Owner XIAN UNIV OF TECH
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