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An integrated model for financial data mining

Author(s)
Cai, Fan  
Le-Khac, Nhien-An  
Kechadi, Tahar  
Uri
http://hdl.handle.net/10197/10846
Date Issued
2012-12-12
Date Available
2019-07-03T08:44:16Z
Abstract
Nowadays, financial data analysis is becoming increasingly importantin the business market. As companies collect more and more data fromdaily operations, they expect to extract useful knowledge from existing collecteddata to help make reasonable decisions for new customer requests, e.g. usercredit category, churn analysis, real estate analysis, etc. Financial institutes haveapplied different data mining techniques to enhance their business performance.However, simple approach of these techniques could raise a performance issue.Besides, there are very few general models for both understanding and forecastingdifferent financial fields. We present in this paper a new classification modelfor analyzing financial data. We also evaluate this model with different realworlddata to show its performance
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Publisher
Springer
Series
Lecture Notes in Computer Science
Volume 7694
Copyright (Published Version)
2012 Springer-Verlag Berlin Heidelberg
Subjects

Data mining

Decision tree

Multilayer perceptron...

Gaussian Process

Classification model

DOI
10.1007/978-3-642-35455-7_28
Web versions
https://dblp.org/db/conf/miwai/miwai2012.html
Language
English
Status of Item
Peer reviewed
Journal
Sombattheera, C., Loi, N.K., Wankar, R., Quan, T. (eds.). Multi-disciplinary Trends in Artificial Intelligence
Conference Details
MIWAI 2012: 12th International Conference, Multi-disciplinary Trends in Artificial Intelligence, Ho Chi Minh City, Vietnam, 26-28 December 2012
ISBN
978-3-642-35454-0
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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insight_publication.pdf

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283.66 KB

Format

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Checksum (MD5)

b60e97692daede717582112d45a92f59

Owning collection
Insight Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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