Infinite decision agent ensemble learning system for credit risk analysis

Li, S; Tsang, IW; Chaudhari, NS

Infinite decision agent ensemble learning system for credit risk analysis

Li, S Tsang, IW

Chaudhari, NS

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011, 2011, 1 pp. 36 - 39
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Li, S	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Chaudhari, NS	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011, 2011, 1 pp. 36 - 39	en_US
dc.identifier.isbn	9780769546070	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120481
dc.description.abstract	Considering the special needs of credit risk analysis, the Infinite DEcision Agent ensemble Learning (IDEAL) system is proposed. In the first level of our model, we adopt soft margin boosting to overcome over fitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron kernel is employed in RVM to generate infinite subagents. Our IDEAL system also shares some good properties, such as good generalization performance, immunity to over fitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011	en_US
dc.relation.isbasedon	10.1109/ICMLA.2011.80	en_US
dc.title	Infinite decision agent ensemble learning system for credit risk analysis	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	1	en_US

Abstract:

Considering the special needs of credit risk analysis, the Infinite DEcision Agent ensemble Learning (IDEAL) system is proposed. In the first level of our model, we adopt soft margin boosting to overcome over fitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron kernel is employed in RVM to generate infinite subagents. Our IDEAL system also shares some good properties, such as good generalization performance, immunity to over fitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy. © 2011 IEEE.

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http://hdl.handle.net/10453/120481