Multi-map Self-Organizing Maps and its application to classification problem
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- Nakagiri Isao
- University of Hyogo
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- Ohtsuka Akitsugu
- University of Hyogo
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- Kamiura Naotake
- University of Hyogo
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- Isokawa Teijiro
- University of Hyogo
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- Matsui Nobuyuki
- University of Hyogo
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- Nishimura Haruhiko
- University of Hyogo
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- Minamide Naoki
- Sysmex Corporation
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- Okamoto Minoru
- Sysmex Corporation
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- Koeda Noriaki
- Sysmex Corporation
Bibliographic Information
- Other Title
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- マップを多重化した自己組織化マップとその分類問題への応用
Abstract
In this paper, we propose a multiple-SOM's (self organizing maps) approach for classification tasks. The proposed approach generates n versions of learning data set by processing an original learning data set. A map is trained using each of the versions, and hence n maps are obtained. Each map brings a result of classification. A global classification is made by means of majority decision of such results. This scheme is applied to defecting confusion between blood samples of different patients. A GA (genetic algorithm) is employed to processing original learning data. Experimental results show that the proposed scheme achieves higher accuracy of confusion defection, compared to the case of detection made by a single map.
Journal
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- Proceedings of the Annual Conference of the Institute of Systems, Control and Information Engineers
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Proceedings of the Annual Conference of the Institute of Systems, Control and Information Engineers SCI04 (0), 214-214, 2004
The Institute of Systems, Control and Information Engineers
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Keywords
Details 詳細情報について
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- CRID
- 1390001205622545792
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- NII Article ID
- 130006982608
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed