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ID 19681
Eprint ID
19681
フルテキストURL
著者
Ise Masayuki Future University Hakodate
Niimi Ayahiko Future University Hakodate
Konishi Osamu Future University Hakodate
抄録
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.
発行日
2009-11-10
出版物タイトル
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
2009巻
1号
出版者
IEEE SMC Hiroshima Chapter
開始ページ
202
終了ページ
207
ISSN
1883-3977
NCID
BB00577064
資料タイプ
会議発表論文
言語
English
著作権者
IEEE SMC Hiroshima Chapter
イベント
5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
イベント地
東広島市
イベント地の別言語
Higashi-Hiroshima City
論文のバージョン
publisher
査読
有り
Eprints Journal Name
IWCIA