Title :
A new model for credit approval problems: A quantum-inspired neuro-evolutionary algorithm with binary-real representation
Author :
De Pinho, Anderson Guimarães ; Vellasco, Marley ; da Cruz, A.V.A.
Author_Institution :
Dept. of Electr. Eng., PUC-Rio, Rio de Janeiro, Brazil
Abstract :
This paper presents a new model for neuro-evolutionary systems. It is a new quantum-inspired evolutionary algorithm with binary-real representation (QIEA-BR) for evolution of a neural network. The proposed model is an extension of the QIEA-R developed for numerical optimization. The Quantum-Inspired Neuro-Evolutionary Computation model (QINEA-BR) is able to completely configure a feed-forward neural network in terms of selecting the relevant input variables, number of neurons in the hidden layer and all existent synaptic weights. QINEA-BR is evaluated in a benchmark problem of financial credit evaluation. The results obtained demonstrate the effectiveness of this new model in comparison with other machine learning and statistical models, providing good accuracy in separating good from bad customers.
Keywords :
credit transactions; evolutionary computation; feedforward neural nets; learning (artificial intelligence); optimisation; quantum computing; binary-real representation; credit approval problems; feedforward neural network; financial credit evaluation; machine learning; neuro-evolutionary systems; numerical optimization; quantum-inspired neuro-evolutionary algorithm; statistical models; Computational modeling; Computer networks; Evolutionary computation; Feedforward neural networks; Feedforward systems; Input variables; Machine learning; Neural networks; Neurons; Quantum computing; classification; genetic algorithms; hybrid neuro-genetic systems; quantum-inspired algorithms;
Conference_Titel :
Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4244-5053-4
DOI :
10.1109/NABIC.2009.5393327
