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Sydney University of Technology Details Findings in Machine Learning (Fuzzy Refinement Domain Adaptation for Long Term Prediction in Banking...

July 1, 2014



Sydney University of Technology Details Findings in Machine Learning (Fuzzy Refinement Domain Adaptation for Long Term Prediction in Banking Ecosystem)

By a News Reporter-Staff News Editor at Journal of Mathematics -- A new study on Machine Learning is now available. According to news reporting originating from Sydney, Australia, by VerticalNews correspondents, research stated, "Long-term bank failure prediction is a challenging real world problem in banking ecosystem and machine learning methods have been recently applied to improve the prediction accuracy. However, traditional machine learning methods assume that the training data and the test data are drawn from the same distribution, which is hard to be met in real world banking applications."

Our news editors obtained a quote from the research from the Sydney University of Technology, "This paper proposes a novel algorithm known as fuzzy refinement domain adaptation to solve this problem based on the ecosystem-oriented architecture. The algorithm utilizes the fuzzy system and similarity/dissimilarity concepts to modify the target instances' labels which were initially predicted by a shift-unaware prediction model. It employs a classifier to modify the label values of target instances based on their similarity/dissimilarity to the candidate positive and negative instances in mixture domains. Thirty six experiments are performed using three different shift-unaware prediction models. In these experiments bank failure financial data is used to evaluate the algorithm."

According to the news editors, the research concluded: "The results demonstrate that the proposed algorithm significantly improves predictive accuracy and outperforms other refinement algorithms."

For more information on this research see: Fuzzy Refinement Domain Adaptation for Long Term Prediction in Banking Ecosystem. IEEE Transactions on Industrial Informatics, 2014;10(2):1637-1646. IEEE Transactions on Industrial Informatics can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - www.ieee.org/; IEEE Transactions on Industrial Informatics - ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424)

The news editors report that additional information may be obtained by contacting V. Behbood, Sydney University of Technology, Decis Syst & E Serv Intelligence Res Lab DESI, Center Quantum Computat & Intelligent Syst QCIS, Sch SoftwareFac Engn & Informat Technol, Sydney, NSW 2007, Australia. Additional authors for this research include J. Lu and G.Q. Zhang.

Keywords for this news article include: Sydney, Cyborgs, Algorithms, Machine Learning, Emerging Technologies, Australia and New Zealand

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2014, NewsRx LLC


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Source: Journal of Mathematics


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