CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
An Ensemble Pattern Classification System Based on Multitree Genetic Programming for Improving Intension Pattern Recognition Using Brain Computer Interaction
Authors
D.P. Muni
J. An
+3 more
K.P. Bennett
P.G. Espejo
W. Banzhaf
Publication date
1 January 2014
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
Abstract
Ensemble learning is one of the successful methods to construct a classification system. Many researchers have been interested in the method for improving the classification accuracy. In this paper, we proposed an ensemble classification system based on multitree genetic programming for intension pattern recognition using BCI. The multitree genetic programming mechanism is designed to increase the diversity of each ensemble classifier. Also, the proposed system uses an evaluation method based on boosting and performs the parallel learning and the interaction by multitree. Finally, the system is validated by the comparison experiments with existing algorithms. © Springer-Verlag Berlin Heidelberg 2014.FALS
Similar works
Full text
Available Versions
Crossref
See this paper in CORE
Go to the repository landing page
Download from data provider
info:doi/10.1007%2F978-3-662-4...
Last time updated on 22/07/2021
DGIST Library Institutional Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:scholar.dgist.ac.kr:20.500...
Last time updated on 12/01/2024