Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool

Abstract: Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. Commonly it has a feature providing alternative designs that are better than the user’s design. In this paper, we propose a novel method in searching alternative design that is by using classification method. The classifiers we use are Naïve Bayes, Decision Tree, and k-Nearest Neighbor. Our experiment shows that Decision Tree has the fastest classification time followed by Naïve Bayes and k-Nearest Neighbor. The differences between classification time of Decision Tree and Naïve Bayes also between Naïve Bayes and k-NN are about an order of magnitude. Based on Percision, Recall, F-measure, Accuracy, and AUC, the performance of Naïve Bayes is the best. It outperforms Decision Tree and k-Nearest Neighbor on all parameters but precision.

Ahmad Ashari, Iman Paryudi and A Min Tjoa. “Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool”. International Journal of Advanced Computer Science and Applications (IJACSA) 4.11 (2013). http://dx.doi.org/10.14569/IJACSA.2013.041105

@article{Ashari2013,
title = {Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.041105},
url = {http://dx.doi.org/10.14569/IJACSA.2013.041105},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {11},
author = {Ahmad Ashari and Iman Paryudi and A Min Tjoa}
}