TY  - JOUR
T1  - Evaluation Performance of Hybrid Localized Multi Kernel SVR (LMKSVR) in
Electrical Load Data Using 4 Different Optimizations
AU - Eko Caraka, Rezzy AU - Abu Bakar, Sakhinah 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 17
SP  - 7440
EP  - 7449
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.7440.7449
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.7440.7449
KW  - SVR
KW  -kernel
KW  -optimization
KW  -electrical
KW  -hybrid
KW  -Malaysia
AB  - The main problem using SVR is to find optimal parameter (σ) by using kernel function such as radial
basis, polynomial, Gaussian and so on. Moreover, we also have to find optimal hyperplane parameter
(C and ε). In the heart of statistical methods and data mining, the motivation of researcher doing this is to
minimize time, money and energy in the analysis at the same time the results will be more accurate. The
development of such a massive technology and the availability of data is very much making progress and
improvement of methods based on data mining and machine learning. In this study, we proposed four different
optimizations such as LIBSVM, MOSEK, QUADPROG, SMO applied to Localized Multi-Kernel Learning
(LMKL) to assign local weights to kernel functions, so that, the best hyperplane parameters will be obtained.
For the simulation, we use the electrical data and we have labeled based on the characteristics of different days
(Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, National Holiday, Ramadhan). As well
as we can capture the pattern of electricity consumption.
ER  -