Multi-dimensional data feature selection method combining genetic algorithm and dragonfly algorithm

Abstract

The invention discloses a multi-dimensional data feature selection method combining a genetic algorithm and a dragonfly algorithm. The method comprises the following specific steps that traffic accident data is simply cleaned; the dragonfly algorithm is embedded into the genetic algorithm to intervene the crossover operation, and the optimal intersection position is found out through the dragonflyalgorithm to improve the optimization speed of the genetic algorithm. A dragonfly algorithm is embedded into a genetic algorithm to intervene mutation operation, and different gene position mutationprobabilities are set by judging whether food and natural enemy gene positions calculated by the dragonfly algorithm are selected or not, so that the convergence rate of the algorithm is increased. The data feature screening result is taken as the input of a multi-dimensional data feature selection method combining a genetic algorithm and a dragonfly algorithm, and the output result is the data feature selected by the algorithm. Experiments prove that the method has good performance for different classifiers, and it is verified that the feature selection method is effective and has robustness.

Description

technical field [0001] The invention relates to a method belonging to machine learning, more precisely, the invention designs a multi-dimensional data feature selection method combining genetic algorithm and dragonfly algorithm. The present invention can not only be applied to the field of machine learning, but also can be extended to other fields, and other fields also belong to the protection scope of this patent. Background technique [0002] Machine learning is an emerging discipline in recent years, involving many fields. Machine learning specializes in the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills and continuously improve their own performance. In machine learning, the selection of data sets is particularly important. For the selection of data sets, the first is to select a data set with enough features and samples, and the second is to perform feature selection in the data set. Since optimization in the...

AI Technical Summary

Problems solved by technology

The researchers applied the genetic algorithm to the feature selection process to obtain a better feature combination, but due to the premature convergence of the genetic algorithm, the feature combination may not be the optimal solution.

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