Character selection method based on particle swarm optimization algorithm

A feature selection method, particle swarm optimization technology, applied in computing, computer components, instruments, etc., to achieve the effect of improving accuracy and reducing the number

Inactive Publication Date: 2016-06-29
NANJING UNIV OF POSTS & TELECOMM
View PDF0 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 op

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Character selection method based on particle swarm optimization algorithm
  • Character selection method based on particle swarm optimization algorithm
  • Character selection method based on particle swarm optimization algorithm

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0025] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.

[0026] Such as figure 1 As shown, the feature selection method based on particle swarm optimization algorithm includes the following steps:

[0027] Step 1. Split the input data set into training set and test set;

[0028] The data is normalized, and the data set is divided into training set and test set. The split method is a leave-one-out cross-validation method. The data set is divided into n parts, one of which is used as the training set, and the remaining n-1 parts are all used as the test set.

[0029] Step 2. Determine the parameters to be optimized and the fitness function based on the s...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 王保云李策高浩
Owner NANJING UNIV OF POSTS & TELECOMM
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap