Fog Forecasting Using Self Growing Neural Network 'CombNET-II:' A Solution for Imbalanced Training Sets Problem

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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4

Como, Italy

July 24-July 27

ISBN: 0-7695-0619-4

	ASCII Text	x
	Anto Satriyo Nugroho, Susumu Kuroyanagi, Akira Iwata, "Fog Forecasting Using Self Growing Neural Network 'CombNET-II:' A Solution for Imbalanced Training Sets Problem," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 4, pp. 4429, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4, 2000.

	BibTex	x
	@article{ 10.1109/IJCNN.2000.860809, author = {Anto Satriyo Nugroho and Susumu Kuroyanagi and Akira Iwata}, title = {Fog Forecasting Using Self Growing Neural Network 'CombNET-II:' A Solution for Imbalanced Training Sets Problem}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {4}, year = {2000}, issn = {1098-7576}, pages = {4429}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.860809}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on TI - Fog Forecasting Using Self Growing Neural Network 'CombNET-II:' A Solution for Imbalanced Training Sets Problem SN - 1098-7576 SP EP A1 - Anto Satriyo Nugroho, A1 - Susumu Kuroyanagi, A1 - Akira Iwata, PY - 2000 VL - 4 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER -

Anto Satriyo Nugroho, Nagoya Institute of Technology

Susumu Kuroyanagi, Nagoya Institute of Technology

Akira Iwata, Nagoya Institute of Technology

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.860809

This paper proposes a method to solve problem that comes with imbalanced training sets which is often seen in the practical applications. We modified Self Growing Neural Network CombNET-II to deal with the imbalanced condition. This model is then applied to practical application, which was launched in '99 Fog Forecasting Contest sponsored by Neurocomputing Technical Group of IEICE, Japan. In this contest, fog event should be predicted every 30 minutes based on the observation of meteorological condition. As the result of the contest, CombNET-II achieved the highest accuracy among the participants and was chosen as the winner of the contest. The advantage of this model is that the independency of the branch networks contributes to an effective way of training and the time can be reduced.

Citation:

Anto Satriyo Nugroho, Susumu Kuroyanagi, Akira Iwata, "Fog Forecasting Using Self Growing Neural Network 'CombNET-II:' A Solution for Imbalanced Training Sets Problem," ijcnn, vol. 4, pp.4429, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4, 2000

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