基于神经网络的半集成学习交通状况分类

S. M. Nasution, E. Husni, Rahadian Yusuf, Kuspriyanto
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引用次数: 1

摘要

技术的发展旨在帮助人类的活动。人类可以利用技术的活动之一是在交通领域,通过实施机器学习。本文讨论了交通状况分类的半集成方法,该方法可用于缩短道路通行时间的交通状况分类。所应用的半集成是使用由多个神经网络组成的投票系统。本文所提方法的性能优于单个神经网络,虽然性能没有明显提高,但在与投票系统半集成的增强中,来自最佳5性能神经网络的增强效果也优于使用10个神经网络的投票系统。准确度由82.58%提高到82.81%,剩余性能值由65.09%提高到65.62%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semi-Ensemble Learning using Neural Network for Classifying Traffic Condition
The growth of technology aims to help human’s activity. One of human’s activity which could use technology is in the transportation area by implementing machine learning. This paper discusses the semi-ensemble method for classifying traffic condition, which could be used to classify the traffic condition for shorten travel time in the road. Semi-ensemble that applied is using voting system which consists of several neural networks. The proposed method in this paper gives better performance result than single neural network Even though the performance result is not increased significantly, enhancement in semi-ensemble with voting system which comes from best-5 performance neural networks also give better result than voting system using 10 neural networks. The performance increased from 82.58% to 82.81% for its accuracy and the rests of performance value increased from 65.09% to 65.62%.
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