Double-accuracy weighted random forest algorithm based on particle swarm optimization

A random forest algorithm and particle swarm optimization technology, applied in the field of data processing, can solve the problems of decision tree uncertainty and unsatisfactory actual effect, and achieve the effect of avoiding the reduction of algorithm accuracy, improving performance, and increasing accuracy

Pending Publication Date: 2020-07-17
SHENYANG LIGONG UNIV
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the randomness of training samples and attributes and the uncertainty of decision tree trees, the actual effect is not ideal.

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
  • Double-accuracy weighted random forest algorithm based on particle swarm optimization
  • Double-accuracy weighted random forest algorithm based on particle swarm optimization
  • Double-accuracy weighted random forest algorithm based on particle swarm optimization

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] In order to enable those skilled in the art to better understand the technical solutions in the application, the technical solutions in the embodiments of the application are clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the application, and Not all examples. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0027] A double-accuracy weighted random forest algorithm based on particle swarm optimization, including the following steps:

[0028] Step1: Determine the original data set D={(x 1 ,y 1 ),(x 2 ,y 2 ),...(x N ,y N )},in, is the input instance, n is the number of features, y i ∈{Y 1 ,Y 2 ,...,Y N} is a class mark, i=1,2,...,N, N is a sample size, randomly determines the number of decision trees K, selects the number of features m, det...

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 double-accuracy weighted random forest algorithm based on particle swarm optimization, and the algorithm comprises the following steps: S1, determining an original data set D, randomly determining the number K of decision trees and the number m of features of the decision trees, and determining a pre-test sample rate X when m is smaller than or equal to n; s2, sampling the training data set Sk by using a Bootstrap sampling method to obtain an out-of-bag data Ok training sub Tk; s3, generating a kth decision tree according to a C4.5 algorithm, and calculating the finalweight of the decision tree; s4, repeating S2 and S3 until the number of decision trees is K; s5, using the decision tree set to test and divide the test data; and S6, taking the accuracy obtained inthe S5 as a fitness value of the particle swarm, performing iterative optimization by adopting a particle swarm optimization algorithm, comparing with historical classification accuracy, and finallyselecting an optimal model parameter. According to the invention, the accuracy of the random forest model is improved; Meanwhile, the problem that the algorithm accuracy is reduced due to parameter selection based on experience is avoided, and the performance of the algorithm is further improved.

Description

technical field [0001] The invention relates to a double-accuracy weighted random forest algorithm based on particle swarm optimization, belonging to the field of data processing. Background technique [0002] Random forest is a supervised ensemble learning classification technique. Its model is composed of a group of decision tree classifiers. The model classifies data by collectively voting on the classification results of a single decision tree to determine the final result. This average voting method may lead to a poorly grown decision tree affecting the final classification result, and it is prone to "tie votes". The traditional random forest algorithm fully guarantees the independence and difference between each decision tree by injecting randomness into the training sample space and attribute space, and overcomes the over-fitting problem of the decision tree well. It also has better robustness. However, due to the randomness of training samples and attributes and th...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/24323G06F18/214
Inventor 张文波冯永新郝颖付立冬王晶
Owner SHENYANG LIGONG UNIV
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