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Airport noise prediction ensemble model based on space fitting and neural network. |
XU Tao1,2, SU Han1, YANG Guo-qing2 |
1. College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China;
2. Information Technology Research Base of Civil Aviation Administration of China, Tianjin 300300, China |
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Abstract This paper proposes an airport noise ensemble prediction model based on space fitting and neural network by introducing ensemble learning method. Space fitting and BP neural network is used respectively to create the base learner and a heterogeneous ensemble algorithm based on observational learning is used to integrate these base learners. The final ensemble model thus can improve prediction accuracy effectively by integrating multiple heterogeneous base prediction learners. The experimental results shows that the proposed heterogeneous ensemble algorithm based on observational learning is better than other heterogeneous ensemble algorithms on accuracy and tolerance for solving the airport noise prediction problem.
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Received: 20 August 2015
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[1] |
过春燕,张邦俊.基于Surfer的机场噪声等值线计算机绘制方法[J]. 中国环境科学, 2003,23(6):631-634.
|
[2] |
徐涛,燕宪金,杨国庆.基于神经网络集成的单个飞行事件噪声预测模型[J]. 中国环境科学, 2014,34(2):539-544.
|
[3] |
温冬琴,王建东.基于奇异谱分析的机场噪声时间序列预测模型[J]. 计算机科学, 2014,41(1):267-270.
|
[4] |
Dai D, Gool L V. Ensemble Projection for Semi-supervised Image Classification[C]//Proceedings of IEEE International Conference on Computer Vision, 2013:2072-2079.
|
[5] |
徐涛,杨奇川,吕宗磊.一种基于动态集成学习的机场噪声预测模型[J]. 电子与信息学报, 2014,36(7):1631-1636.
|
[6] |
Bounova G, Weck O. Overview of Metrics and Their Correlation Patterns for Multiple-metric Topology Analysis on Heterogeneous Graph Ensembles[J]. Physical Review E, 2012, 85(1):161-170.
|
[7] |
Nelson J P. Meta-analysis of Airport Noise and Hedonic Property Values[J]. Journal of Transport Economics and Policy, 2004, 38(1):1-27.
|
[8] |
Li X, Lan S, Li G, et al. Simulation of Aircraft Flight Track by BéZier and B-spline Curve[J]. Journal of Southwest Jiaotong University, 2011,46(6):1040-1045.
|
[9] |
Bishop C M. Training with Noise is Equivalent to Tikhonov Regularization[J]. Neural Computation, 1995,7(1):108-116.
|
[10] |
Jang M, Cho S. Ensemble Learning Using Observational Learning Theory[M]. Proceedings of IEEE International Joint Conference on Neural Networks, 1999,2:1287-1292.
|
[11] |
Galar M, Fernandez A, Barrenechea E, et al. A Review on Ensembles for the Class Imbalance Problem:Bagging-, Boosting-, and Hybrid-based Approaches[J]. Part C:Applications and Reviews, IEEE Transactions on Systems, Man, and Cybernetics, 2012,42(4):463-484.
|
[12] |
Dietterich T G. Machine Learning Research:Four Current Directions[J]. AI Magazine, 1997,18(4):97-136.
|
[13] |
Ibrahim N, Wibowo A. Partial Least Squares Regression based Variables Selection for Water Level Predictions[J]. American Journal of Applied Science, 2013,10(4):322-330.
|
[14] |
Guo Z, Wu J, Lu H, et al. A Case Study on a Hybrid Wind Speed Forecasting Method Using BP Neural Network[J]. Knowledge-based Systems, 2011,24(7):1048-1056.
|
[15] |
邓万宇,郑庆华,陈琳,等.神经网络极速学习方法研究[J]. 计算机学报, 2010,33(2):279-287.
|
[16] |
Ding S, Su C, Yu J. An Optimizing BP Neural Network Algorithm based on Genetic Algorithm[J]. Artificial Intelligence Review, 2011,36(2):153-162.
|
[17] |
Zhao Q L, Jiang Y H, Xu M. Incremental Learning by Heterogeneous Bagging Ensemble[M]. Advanced Data Mining and Applications. Berlin:Springer-Verlag, 2010:1-12.
|
[18] |
Nguyen H V, Ang H H, Gopalkrishnan V. Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces[R]. Proceedings of the 15th International Conference of Database Systems for Advanced Applications, 2010:368-383.
|
|
|
|