ID | 19681 |
Eprint ID | 19681
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FullText URL | |
Author |
Ise Masayuki
Niimi Ayahiko
Konishi Osamu
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Abstract | There is increased interest in accurate model acquisition from large scale data streams. In this paper, because we have focused attention on time-oriented variation, we propose a method contracting time-series data for data stream. Additionally, our proposal method employs the combination of plural simple contraction method and original features. In this experiment, we treat a real data stream in credit card transactions because it is large scale and difficult to classify. This experiment yields that this proposal method improves classification performance according to training data. However, this proposal method needs more generality. Hence, we'll improve generality with employing the suitable combination of a contraction method and a feature for the feature in our proposal method.
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Published Date | 2009-11-10
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Publication Title |
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
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Volume | volume2009
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Issue | issue1
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Publisher | IEEE SMC Hiroshima Chapter
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Start Page | 202
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End Page | 207
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ISSN | 1883-3977
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NCID | BB00577064
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Content Type |
Conference Paper
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language |
English
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Copyright Holders | IEEE SMC Hiroshima Chapter
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Event Title | 5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
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Event Location | 東広島市
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Event Location Alternative | Higashi-Hiroshima City
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File Version | publisher
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Refereed |
True
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Eprints Journal Name | IWCIA
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