Feature Selection in Large Scale Data Stream for Credit Card Fraud Detection
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
Page 202-207
published_at 2009-11
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| File | |
| Title ( eng ) |
Feature Selection in Large Scale Data Stream for Credit Card Fraud Detection
|
| Creator |
Ise Masayuki
Niimi Ayahiko
Konishi Osamu
|
| Source Title |
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
|
| Start Page | 202 |
| End Page | 207 |
| 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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| NDC |
Technology. Engineering [ 500 ]
|
| Language |
eng
|
| Resource Type | conference paper |
| Publisher |
IEEE SMC Hiroshima Chapter
|
| Date of Issued | 2009-11 |
| Rights |
(c) Copyright by IEEE SMC Hiroshima Chapter.
|
| Publish Type | Version of Record |
| Access Rights | open access |
| Source Identifier |
[ISSN] 1883-3977
[URI] http://www.hil.hiroshima-u.ac.jp/iwcia/2009/
|
