Obtaining Classification Rules Using LVQ+PSO: An Application to Credit Risk
作者： Laura Lanzarini , Augusto Villa-Monte , Aurelio Fernández-Bariviera , Patricia Jimbo-Santana
DOI: 10.1007/978-3-319-19704-3_31
关键词: Order (exchange) 、 Set (abstract data type) 、 Key (cryptography) 、 Work (electrical) 、 Artificial neural network 、 Learning vector quantization 、 Credit risk 、 Operations research 、 Computer science 、 Decision-making
摘要: Credit risk management is a key element of financial corporations. One the main problems that face credit officials to approve or deny petition. The usual decision making process consists in gathering personal and information about borrower. This paper present new method able generate classifying rules work no only on numerical attributes, but also nominal attributes. method, called LVQ+PSO, combines competitive neural network with an optimization technique order find reduced set rules. These constitute predictive model for approval. Given quantity rules, our very useful officers aiming make decisions granting credit. Our was applied two databases were extensively analyzed by other competing classification methods. We obtain satisfactory results. Future research lines are exposed.
springer.com
sci-hub.se 参考文章(16)
Felipe de Jesús Nuñez Cárdenas, Introducción a la Minería de Datos ,(2011)
Laura Lanzarini, Javier López, Juan Andrés Maulini, Armando De Giusti, A new binary PSO with velocity control international conference on swarm intelligence. pp. 111- 119 ,(2011) , 10.1007/978-3-642-21515-5_14
Ramakrishnan Srikant, Rakesh Agrawal, Fast Algorithms for Mining Association Rules in Large Databases very large data bases. pp. 487- 499 ,(1994)
Ian H. Witten, Eibe Frank, Generating Accurate Rule Sets Without Global Optimization international conference on machine learning. pp. 144- 151 ,(1998)
Mark A. Hall, Ian H. Witten, Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques ,(1999)
Gilles Venturini, SIA: a supervised inductive algorithm with genetic search for learning attributes based concepts european conference on machine learning. pp. 280- 296 ,(1993) , 10.1007/3-540-56602-3_142
Ziqiang Wang, Xia Sun, Dexian Zhang, A PSO-Based Classification Rule Mining Algorithm international conference on intelligent computing. ,vol. 4682, pp. 377- 384 ,(2009) , 10.1007/978-3-540-74205-0_42
T. Kohonen, The self-organizing map Proceedings of the IEEE. ,vol. 78, pp. 1464- 1480 ,(1990) , 10.1109/5.58325
Chihli Hung, Lynn Huang, Extracting Rules from Optimal Clusters of Self-Organizing Maps 2010 Second International Conference on Computer Modeling and Simulation. ,vol. 1, pp. 382- 386 ,(2010) , 10.1109/ICCMS.2010.92
Hong Yu, Xiaolei Huang, Xiaorong Hu, Hengwen Cai, A Comparative Study on Data Mining Algorithms for Individual Credit Risk Evaluation international conference on management of e-commerce and e-government. pp. 35- 38 ,(2010) , 10.1109/ICMECG.2010.16