TY - GEN
T1 - Predicting smart home lighting behavior from sensors and user input using very fast decision tree with Kernel Density Estimation and improved Laplace correction
AU - Dinata, Ida Bagus Putu Peradnya
AU - Hardian, Bob
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/3/23
Y1 - 2014/3/23
N2 - One way to predict the behavior of smart home lighting is by using machine learning. Currently many methods of supervised learning that used for this problem, one of them is decision tree method. Very Fast Decision Tree (VFDT) as one of the decision tree method that has advantages in online machine learning that may useful in smart home, but there are still some room of improvisation that can improve accuracy of VFDT. The experiment result is obtained that VFDT is better than Naïve Bayes (NB) and Artificial Neural Network (ANN) in offline and online experiment. In addition, Kernel Density Estimation (KDE) and improved Laplace correction that is used as improvisation of VFDT is able to increase the accuracy and Matthews Correlation Coefficient (MCC) of VFDT in predicting smart home lighting switch usage.
AB - One way to predict the behavior of smart home lighting is by using machine learning. Currently many methods of supervised learning that used for this problem, one of them is decision tree method. Very Fast Decision Tree (VFDT) as one of the decision tree method that has advantages in online machine learning that may useful in smart home, but there are still some room of improvisation that can improve accuracy of VFDT. The experiment result is obtained that VFDT is better than Naïve Bayes (NB) and Artificial Neural Network (ANN) in offline and online experiment. In addition, Kernel Density Estimation (KDE) and improved Laplace correction that is used as improvisation of VFDT is able to increase the accuracy and Matthews Correlation Coefficient (MCC) of VFDT in predicting smart home lighting switch usage.
UR - http://www.scopus.com/inward/record.url?scp=84979520625&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS.2014.7065885
DO - 10.1109/ICACSIS.2014.7065885
M3 - Conference contribution
AN - SCOPUS:84979520625
T3 - Proceedings - ICACSIS 2014: 2014 International Conference on Advanced Computer Science and Information Systems
SP - 171
EP - 175
BT - Proceedings - ICACSIS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014
Y2 - 18 October 2014 through 19 October 2014
ER -