A k-Nearest Neighbours Based Ensemble via Optimal Model Selection for Regression

A k-Nearest Neighbours Based Ensemble via Optimal Model Selection for Regression

Ensemble methods based on k-NN models minimise the effect of outliers in a training dataset by searching groups of the k closest data points to estimate the response of an unseen observation. However, traditional k-NN based ensemble methods use the arithmetic mean of the training points' respon...

Full description

Bibliographic Details
Main Authors: Amjad Ali, Muhammad Hamraz, Poom Kumam, Dost Muhammad Khan, Umair Khalil, Muhammad Sulaiman, Zardad Khan
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-NN
random <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-NN
regression
stepwise model selection
ensemble learning
non-informative features
Online Access:https://ieeexplore.ieee.org/document/9143105/

Internet

https://ieeexplore.ieee.org/document/9143105/

Similar Items