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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/5025
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| タイトル: | An agent-based approach for predictions based on multi-dimensional complex data |
| 著者: | Ma, Tieju
Nakamori, Yoshiteru |
| キーワード: | agent-based approach
membership function
prediction |
| 発行日: | 2006-05-08 |
| 出版者: | Elsevier |
| 誌名: | Information Sciences |
| 巻: | 176 |
| 号: | 9 |
| 開始ページ: | 1156 |
| 終了ページ: | 1174 |
| DOI: | 10.1016/j.ins.2005.07.011 |
| 抄録: | This paper presents an agent-based approach to the identification of prediction models for continuous values from multi-dimensional data, both numerical and categorical. A simple description of the approach is: a number of agents are sent to the data space to be investigated. At the micro-level, each agent tries to build a local linear model with multi-linear regression by competing with others; then at the macro-level all surviving agents build the global model by introducing membership functions. Three tests were carried out and the performance of the approach was compared with a neural network. The results of the three tests show that the agent-based approach can achieve good performance for some data sets. The approach complements rather than competes with existing Soft Computing methods. |
| Rights: | NOTICE: This is the author’s version of a work accepted for publication by Elsevier.
Changes resulting from the publishing process, including peer review, editing, corrections,
structural formatting and other quality control mechanisms, may not be reflected in this
document. Changes may have been made to this work since it was submitted for publication.
A definitive version was subsequently published in Tieju Ma, Yoshiteru Nakamori and Wei Huang, Information Sciences, 176(9), 2006, 1156-1174, http://dx.doi.org/10.1016/j.ins.2005.07.011 |
| URI: | http://hdl.handle.net/10119/5025 |
| 資料タイプ: | author |
| 出現コレクション: | a10-1. 雑誌掲載論文 (Journal Articles)
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このアイテムのファイル:
| ファイル |
記述 |
サイズ | 形式 |
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| C10100.pdf | | 585Kb | Adobe PDF | 見る/開く |
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