A technique of fuzzy C-Mean in multiple linear regression model toward paddy yield
. In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2018
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7006/1/P9889_718ddb01ea16506f15e4ff1dc78fd2b7.pdf http://eprints.uthm.edu.my/7006/ https://doi.org/10.1088/1742-6596/995/1/012010 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tun Hussein Onn Malaysia |
| Language: | English |
| Summary: | . In this paper, we propose a hybrid model which is a combination of multiple linear
regression model and fuzzy c-means method. This research involved a relationship between 20
variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer
rates. Data used were from the multi-location trials for rice carried out by MARDI at major
paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing
observations were estimated using mean estimation techniques. The data were analyzed using
multiple linear regression model and a combination of multiple linear regression model and
fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is
normally scattered without multicollinearity among independent variables. Analysis of fuzzy c�means cluster the yield of paddy into two clusters before the multiple linear regression model
can be used. The comparison between two method indicate that the hybrid of multiple linear
regression model and fuzzy c-means method outperform the multiple linear regression model
with lower value of mean square error. |
|---|