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Effective hybrid feature selection method based on chi-square detection algorithm and improved fruit fly optimization algorithm

A fruit fly optimization algorithm and chi-square detection technology, applied in the field of bioinformatics, can solve problems such as the inability to select the optimal feature subset classification accuracy, and achieve the effects of increasing diversity, simplifying quantity, and enhancing search capabilities

Inactive Publication Date: 2020-07-14
HENAN UNIVERSITY
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  • Application Information

AI Technical Summary

Problems solved by technology

[0008] In order to solve the problem that many intelligent algorithms cannot balance the two goals of selecting the optimal feature subset and improving the classification accuracy, the present invention provides an effective hybrid feature selection based on the chi-square detection algorithm and the improved fruit fly optimization algorithm method, which can further improve the classification accuracy of features while selecting the optimal feature subset

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  • Effective hybrid feature selection method based on chi-square detection algorithm and improved fruit fly optimization algorithm
  • Effective hybrid feature selection method based on chi-square detection algorithm and improved fruit fly optimization algorithm
  • Effective hybrid feature selection method based on chi-square detection algorithm and improved fruit fly optimization algorithm

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Embodiment 1

[0062] The effective hybrid feature selection method (CS-IFOA method for short) based on chi-square detection algorithm and improved fruit fly optimization algorithm proposed by the present invention, such as figure 1 shown, including the following steps:

[0063] S101. Randomly initialize M individuals in the population by adopting a feature sorting strategy based on the chi-square detection algorithm, where M represents the total number of fruit fly individuals in the population;

[0064] S102. Calculate the fitness value of each individual in the population by using the set fitness function, and use the solution represented by the individual with the largest fitness value in the population as the global optimal solution;

[0065] S103. Use the improved fruit fly optimization algorithm to update the individuals in the population, and update the fitness value of each individual in the population, and update the global optimal solution in the population;

[0066] S104. Taking...

Embodiment 2

[0069] On the basis of the above-mentioned embodiment 1, the effective mixed feature selection method based on the chi-square detection algorithm and the improved fruit fly optimization algorithm provided by the embodiment of the present invention includes the following steps:

[0070] S201. Randomly initialize M individuals in the population by adopting a characteristic sorting strategy based on the chi-square detection algorithm;

[0071] Specifically, this step includes the following sub-steps:

[0072] S2011. Calculate the chi-square value of each attribute in the data set according to the chi-square detection algorithm;

[0073] Specifically, calculate the chi-square value of each attribute according to formula (1):

[0074]

[0075] Among them, A i is the observation frequency of level i, E i is the expected frequency of level i, r is the total frequency, p i is the expected frequency of level i. E. i =r×p i , k is the number of cells. x 2 The value indicates...

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Abstract

The invention provides an effective hybrid feature selection method based on a chi-square detection algorithm and an improved fruit fly optimization algorithm. The method comprises: the step 1, randomly initializing M individuals in a population by adopting a chi-square detection algorithm-based feature sorting strategy; the step 2, calculating a fitness value of each individual in the populationby adopting a set fitness function, and taking a solution expressed by the individual with the maximum fitness value in the population as a global optimal solution; the step 3, updating individuals inthe population by adopting an improved fruit fly optimization algorithm, updating a fitness value of each individual in the population, and updating a global optimal solution in the population; and the step 4, taking the step 3 as one iteration, and repeating the step 3 until the current number of iterations reaches the set number of iterations. Compared with other feature selection methods, themethod has the advantage that higher classification accuracy can be obtained by using fewer features.

Description

technical field [0001] The invention relates to the technical field of bioinformatics, in particular to an effective mixed feature selection method based on a chi-square detection algorithm and an improved fruit fly optimization algorithm. Background technique [0002] In the control and management of diseases, disease data analysis plays an increasingly important role in early diagnosis, especially for diseases such as cancer. There is an urgent need for more reliable auxiliary methods, combined with medical diagnosis, to maximize the accuracy of disease diagnosis. With the rapid development of biomedical technology and key technologies in the field of health, a large amount of bioinformatics and clinical medical data, especially molecular biology experiment data, has grown and accumulated at an unprecedented speed and scale. These medical big data contain a lot of valuable information. Data mining of these data will help to discover the pathogenesis, risk factors and the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2111G06F18/2113G06F18/24147
Inventor 阎朝坤吴彬侯金翠罗慧敏王建林
Owner HENAN UNIVERSITY