Predicting smart home lighting behavior from sensors and user input using very fast decision tree with Kernel Density Estimation and improved Laplace correction

Ida Bagus Putu Peradnya Dinata, Bob Hardian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - ICACSIS 2014
Subtitle of host publication2014 International Conference on Advanced Computer Science and Information Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages171-175
Number of pages5
ISBN (Electronic)9781479980758
DOIs
Publication statusPublished - 23 Mar 2014
Event2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014 - Jakarta, Indonesia
Duration: 18 Oct 201419 Oct 2014

Publication series

NameProceedings - ICACSIS 2014: 2014 International Conference on Advanced Computer Science and Information Systems

Conference

Conference2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014
Country/TerritoryIndonesia
CityJakarta
Period18/10/1419/10/14

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