Character selection method based on particle swarm optimization algorithm

Abstract

The invention discloses a character selection method based on a particle swarm optimization algorithm. The method comprises steps of 1: preprocessing an input data set and dividing the data set into a training set and a test set; 2: determining a to-be-optimized parameter and a fitness function based on a unique characteristic selection method, establishing and initializing first generation of a particle swarm, and then carrying out iteration; 3: according to the fitness function, calculating fitness and an individual optimal position of each of particles and overall optimal positions of all particles; 4: by using the iteration formula of the particle swarm, updating speed and a position vector of each of the particles, the individual optimal of each of the particles and overall optimal positions of all the particles; 5: repeating steps 2-4 until reaching to the maximum iteration time; and 6: outputting the optimal solution. According to the invention, the numbers of to-be-selected characteristics are introduced into the fitness function, so precision of classification can be improved and the numbers of the to-be-selected characteristics can be reduced.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a feature selection method based on a particle swarm optimization algorithm. Background technique [0002] In the pattern system classification system, the input data often contains a large number of features, but only a small part of the features are related to the classification, and a large number of irrelevant features will cause the "dimension disaster" and reduce the new noise ratio. Feature selection can eliminate irrelevant or redundant features, so as to reduce the number of features, improve model accuracy, and reduce running time. On the other hand, selecting the truly relevant features simplifies the model and makes it easier for researchers to understand the process of data generation. Feature selection, also known as feature subset selection, or attribute selection, refers to selecting a subset of features from all features, and this subset has better or t...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a feature selection method based on the particle swarm optimization algorithm to solve the defects of the above methods, especially for reducing the number of features used

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