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A Solution for Imbalanced Training Sets Problem by CombNET-II and Its Application on Fog Forecasting
https://nitech.repo.nii.ac.jp/records/5072
https://nitech.repo.nii.ac.jp/records/5072b975069d-1929-46ba-b259-fbd661337cae
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文_fulltext (989.4 kB)
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Copyright(c)2002 IEICE http://search.ieice.org/index.html
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||||
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| 公開日 | 2012-11-07 | |||||||||||
| タイトル | ||||||||||||
| タイトル | A Solution for Imbalanced Training Sets Problem by CombNET-II and Its Application on Fog Forecasting | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | journal article | |||||||||||
| 著者 |
Nugroho, Anto Satriyo
× Nugroho, Anto Satriyo
× Kuroyanagi, Susumu
× Iwata, Akira
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| 著者別名 | ||||||||||||
| 姓名 | 黒柳, 奨 | |||||||||||
| 著者別名 | ||||||||||||
| 姓名 | 岩田, 彰 | |||||||||||
| bibliographic_information |
en : IEICE transactions on information and systems 巻 E85-D, 号 7, p. 1165-1174, 発行日 2002-07-01 |
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| 出版者 | ||||||||||||
| 出版者 | Institute of Electronics, Information and Communication Engineers | |||||||||||
| 言語 | en | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||
| 収録物識別子 | 0916-8532 | |||||||||||
| item_10001_source_id_32 | ||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||
| 収録物識別子 | AA10826272 | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| 内容記述 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | Studies on artificial neural network have been conducted for a long time, and its contribution has been shown in many fields. However, the application of neural networks in the real world domain is still a challenge, since nature does not always provide the required satisfactory conditions. One example is the class size imbalanced condition in which one class is heavily under-represented compared to another class. This condition is often found in the real world domain and presents several difficulties for algorithms that assume the balanced condition of the classes. In this paper, we propose a method for solving problems posed by imbalanced training sets by applying the modified large-scale neural network CombNET-II. CombNET-II consists of two types of neural networks. The first type is a one-layer vector quantization neural network to turn the problem into a more balanced condition. The second type consists of several modules of three-layered multilayer perceptron trained by backpropagation for finer classification. CombNET-II combines the two types of neural networks to solve the problem effectively within a reasonable time. The performance is then evaluated by turning the model into a practical application for a fog forecasting problem. Fog forecasting is an imbalanced training sets problem, since the probability of fog appearance in the observation location is very low. Fog events should be predicted every 30 minutes based on the observation of meteorological conditions. Our experiments showed that CombNET-II could achieve a high prediction rate compared to the k-nearest neighbor classifier and the three-layered multilayer perceptron trained with BP. Part of this research was presented in the 1999 Fog Forecasting Contest sponsored by Neurocomputing Technical Group of IEICE, Japan, and CombNET-II achieved the highest accuracy among the participants. | |||||||||||
| 言語 | en | |||||||||||
