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• W2127096916 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." @default.
• W2127096916 created "2016-06-24" @default.
• W2127096916 creator A5051536816 @default.
• W2127096916 creator A5060962200 @default.
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• W2127096916 date "2013-01-01" @default.
• W2127096916 modified "2023-09-27" @default.
• W2127096916 title "Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool" @default.
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• W2127096916 doi "https://doi.org/10.14569/ijacsa.2013.041105" @default.
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