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High-dimensional data function selection algorithm based on reverse elimination method and application thereof in medical treatment

A technology for high-dimensional data and selection algorithms, applied in special data processing applications, electrical digital data processing, computing, etc., to achieve the effect of reducing prediction errors

Inactive Publication Date: 2014-01-08
胡膺期
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Problems solved by technology

However, it has a disadvantage: variable interactions usually appear together with modular effects, so the effects of some variables must be considered together with other variables to be detected

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  • High-dimensional data function selection algorithm based on reverse elimination method and application thereof in medical treatment
  • High-dimensional data function selection algorithm based on reverse elimination method and application thereof in medical treatment
  • High-dimensional data function selection algorithm based on reverse elimination method and application thereof in medical treatment

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Embodiment Construction

[0036] First of all, the high-dimensional data function selection algorithm based on the backward dropping method and its application in medical treatment of the present invention are based on two basic tools: influence measure and backward dropping algorithm.

[0037] A. Measuring Influence

[0038] In order to explain the influence measure clearly, assume that the dependent variable is dichotomous (only takes the value 0 or 1, for example, 0 means no cancer, 1 has cancer. Depending on the problem, it can be a continuous variable or a discrete variable with more values). All covariates (such as the expression levels of various genes in genetically predicting disease) are discrete. Consider splitting yes a subset of covariates . If all variables in this subset are dichotomous, then this split has 2

[0039] possible values. definition For when the split value is Time number of observations. Assume that the covariates of this split set are the same as it doe...

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Abstract

The invention discloses a high-dimensional data function selection algorithm based on a reverse elimination method and application of the high-dimensional data function selection algorithm in medical treatment. The high-dimensional data function selection algorithm comprises three stages. In the first stage, according to a variable selection method based on interaction, influential factors which can interact with other factors to form function modules are firstly recognized. In the second stage, function modules are generated through the reverse elimination method, through the influential factors generated in the first stage, influential function modules which can form high influences with the influential factors are selected, the factors in the function modules interact with each other, and therefore a strong correlation on dependent variables is generated. In the third stage, classifiers are combined, one function module forms one classifier, and the classifiers are combined to form classification rules on the dependent variables. The high-dimensional data function selection algorithm can provide quantitative results for genetic diagnosis and treatment in medical treatment and health, and predication accuracy is greatly improved.

Description

technical field [0001] The invention relates to technologies in the fields of prediction, classification and clustering, in particular to a high-dimensional data function selection algorithm based on a reverse elimination method and its application in medical treatment. Background technique [0002] High-dimensional data mainly means that the number of individuals recorded in the data is much smaller than the type of attributes of each individual. For example, in gene prediction and diagnosis, the data includes normal people and patients. The total number of people is the number of individuals in the data, but each individual has millions of genes. Due to cost factors, it is impossible to have genetic information of millions of individuals. How to find some genes that can truly predict diseases from millions of genes on the premise of a small number of individuals is no longer a conventional statistical algorithm. problems that can be solved. Not only is prediction so chal...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 胡膺期
Owner 胡膺期
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