Effective mixed feature selection method based on elite flower pollination algorithm and ReliefF

Abstract

The invention provides an effective mixed feature selection method based on an elite flower pollination algorithm and ReliefF. The method comprises the following steps: step 1, initializing a population composed of M individuals by adopting a double-initial population strategy based on ReliefF feature sorting and randomization; step 2, updating the population by adopting a binary elite flower pollination algorithm, and calculating a fitness value of each individual in the population to obtain a global optimal solution in the population; step 3, searching a neighborhood of the globally optimalsolution by adopting a tabu search algorithm to determine a candidate solution, and updating a tabu table according to the fitness value of the candidate solution; step 4, selecting an individual withthe maximum fitness value from the tabu table as an elite individual, and replacing the individual with the minimum fitness value in the population with the elite individual to form a new population;and step 5, taking the step 2 to the step 4 as one iteration, and repeating the step 2 to the step 4 until the current iteration frequency reaches the set iteration frequency. Compared with other feature selection methods, the method can obtain high classification accuracy.

Description

technical field [0001] The invention relates to the technical field of bioinformatics, in particular to an effective mixed feature selection method based on elite flower pollination algorithm and ReliefF. Background technique [0002] 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 interaction between the disease, and provide a basis for the clinical diagnosis and treatment of the disease. for reference. In recent years, researchers in related fields have analyzed microarray data, and proved the feasibility and effectiveness of the proposed method through comparative analysis of experimental results, providing a l...

AI Technical Summary

Problems solved by technology

[0018] For the existing feature selection algorithm, it is impossible to cover the two aspects of "selecting the most valuable feature subset composed of relevant features from the original input data" and "improving the classification accuracy as much as possible". To solve the problem of the target, the present invention provides an effective mixed feature selection method based on the elite flower pollination algorithm and ReliefF, which can further improve the classification accuracy of features while selecting the optimal feature subset

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