.png){kind=icon}
Return
Fuzzy ELM for classification based on feature space
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-019-08321-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As a competitive machine learning algorithm, extreme learning machine (ELM), with its simple theory and easy implementation, has been widely used in the field of pattern accuracy. Recently, researchers have proposed related research algorithms to accommodate noise and outlier data. With a proper fuzzy membership function, a fuzzy ELM can effectively reduce the effects of outliers when solving the classification problem. However, how to apply ELM for learning and accuracy in the presence of noise is still an important research topic.A novel fuzzy ELM (ANFELM) technique is proposed in this paper. In the algorithm, the membership degree of the sample is calculated in a feature mapping space instead of the data input space. The algorithm provides good performance in reducing the effects of outliers and significantly improves classification accuracy and generalization. Experiments on UCI datasets and textual datasets show that the proposed algorithm significantly improves the classification capability of ELM and is superior to other algorithms.
Keywords:
Extreme learning machine
Classification
Membership degree
Feature mapping space
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
2.0W
Citations:
3.2W
Organization
Cited Papers
Productivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materials.png)
Desalination
OAAI
DesalinationIF0
OAAIShareSaveLinear vs. Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral ImagesSENSORSOAAI
SENSORSIF3.5
OAAIShareSaveShareSaveShareSaveRobust activation function and its application: Semi-supervised kernel extreme learning methodNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSave
View more