Genetic algorithm and variable precision rough set-based PET/CT high-dimensional feature level selection method

A genetic algorithm and rough set technology, applied in the field of PET/CT high-dimensional feature level selection, can solve problems such as variable precision roughness, achieve the effect of perfecting the concept and enhancing the ability to deal with noise

Inactive Publication Date: 2018-02-09
NINGXIA MEDICAL UNIV
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Problems solved by technology

[0009] 3. Variable precision rough set

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  • Genetic algorithm and variable precision rough set-based PET/CT high-dimensional feature level selection method
  • Genetic algorithm and variable precision rough set-based PET/CT high-dimensional feature level selection method
  • Genetic algorithm and variable precision rough set-based PET/CT high-dimensional feature level selection method

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Embodiment Construction

[0097] The present invention will be further described below in conjunction with the accompanying drawings.

[0098] The invention protects the PET / CT high-dimensional feature-level selection method based on genetic algorithm and variable precision rough set, including the following steps:

[0099] 1. Parameter setting: including population size M, chromosome length (that is, the number of conditional attributes) N, crossover probability Pc, mutation probability Pm, fitness function F(x), and the maximum number of iterations K of the population.

[0100] 2. Encoding: Binary encoding method is adopted, expressed by a binary string whose length is equal to the number of conditional attributes, each of which corresponds to a conditional attribute, a certain bit takes 1 to indicate that the conditional attribute corresponding to this bit is selected, and 0 indicates that the corresponding conditional attribute is not selected. The bit corresponds to the condition attribute. For e...

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Abstract

The invention discloses a genetic algorithm and variable precision rough set-based PET/CT high-dimensional feature level selection method. According to the method, on one hand, a chromosome coding value, a minimum reduction number of attributes, attribute dependency and the like are comprehensively considered to construct a universal fitness function framework, and different fitness functions arerealized by adjusting weight coefficients of factors; and on the other hand, for the limitation of a Pawlak rough set model, a classification error rate beta is introduced for broadening strict inclusion of lower approximation in the Pawlak rough set model to partial inclusion, so that the concept of an approximation space is perfected, the noise processing capability is enhanced, and the beta range is continuously changed to realize different fitness functions. Experimental results show that different weight coefficients greatly influence the results under the condition of consistent classification error rate; and likewise, under the condition of consistent weight coefficient, the classification error rate is increased constantly, the experimental results have relatively large difference,and a parameter combination most suitable for the method can be found according to data in the method.

Description

technical field [0001] The invention relates to the field of algorithm optimization, in particular to a PET / CT high-dimensional feature-level selection method based on genetic algorithm and variable precision rough set. Background technique [0002] Rough set is a mathematical tool to deal with fuzzy and uncertain knowledge. Its main idea is to derive the decision-making or classification rules of the problem through knowledge reduction under the premise of keeping the classification ability unchanged. [1] , but a limitation of the Pawlak rough set model is that the classification it deals with must be completely correct or positive, that is, only consider "completely contained" and "not contained at all", rather than "belongs to" and "to some extent" contains" relationship [2] . On the basis of the Pawlak rough set model, Ziarko proposed the extension of the Pawlak rough set model—Variable Precision Rough Set Model (Variable Precision Rough SetModel), which introduces the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/24G06N3/08
CPCG06N3/086G16B40/00
Inventor 周涛陆惠玲王媛媛师宏斌
Owner NINGXIA MEDICAL UNIV
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