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Rio de Janeiro, 11 de Dezembro de 2009. A New Model for Credit Approval Problems: A Neuro-Genetic System with Quantum Inspiration and Binary-Real Representation Anderson Pinho, Marley Vellasco e André Vargas
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A New Model for Credit Approval Problems: A Neuro-Genetic System with Quantum Inspiration and Binary-Real Representation
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A New Model for Credit Approval Problems: A Neuro-Genetic System with Quantum Inspiration and Binary-Real Representation
Introduction ,[object Object],“ The main objective is to separate good from bad customers, rejecting credit to customers classified as bad, minimizing risk of the firms and banks.” Many techniques and areas of science has been used for evaluating credit approval problems. We could list: Discriminant Analysis, Logistic Regression, Mathematical Programming, Expert System,  Neural Networks, Genetic Programming, Non-Parametric Methods, Model of Time Series,  Hybrid Models, Evolutionary Programming, Support Vector Machines, Recursive Partitioning ...
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A New Model for Credit Approval Problems: A Neuro-Genetic System with Quantum Inspiration and Binary-Real Representation
The Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Problem ,[object Object],x 1 x 2 x 3 x n C 1 C 2
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A New Model for Credit Approval Problems: A Neuro-Genetic System with Quantum Inspiration and Binary-Real Representation
The Algorithm ,[object Object]
The Algorithm ,[object Object],Real Part Binary Part
The Algorithm ,[object Object]
The Algorithm ,[object Object],Real Part Binary Part Let “random” be a random number generated for each classic individual observated in the quantum population Gene(i)= (Center(i) – Height(i)/2) +  Height  * random Gene(i)= If random between 0 and (Q-bit 0)^2 then 1 else 0
The Algorithm ,[object Object]
The Algorithm ,[object Object]
The Algorithm ,[object Object]
The Algorithm ,[object Object],Real Part Binary Part Individual 1 Real Part Binary Part Individual 2 Real Part Binary Part Soon
The Algorithm ,[object Object],Binary Part Individual 1 Binary Part Individual 2 Binary Part Soon Let “random” be a random number generated for each classic individual observated in the quantum population Let “Individual 1” be a classic individual inside population of the best. SoonGene(1)= If random > CrossoverProbability then “Change Genes” Else “Don´t Change The Genes”.
The Algorithm ,[object Object],Real Part Individual 1 Real Part Individual 2 Real Part Soon Let “random” be a random number generated for each classic individual observated in the quantum population SoonGene(1)= Indiv1Gene(1)+(Indiv2Gene(1)-Indiv1Gene(1))*random Let “Individual 1” be a classic individual inside population of the best.
The Algorithm ,[object Object]
The Algorithm ,[object Object],Real Part Binary Part Real Part Binary Part Quantum Individual Classic Individual
The Algorithm ,[object Object],Binary Part Binary Part Quantum Individual Classic Individual Quantum Q-Gates  “ For the first gene, for example,  we grows up the probability of a Q-Bit 1 be choosen,  because we have observed a Bit-1 on  classic individual from the population of best, wich was choosen randomly”
The Algorithm ,[object Object],Real Part Real Part Quantum Individual Classic Individual UCenter(1) = Center(1) + (Real(1) – Center(1)) * random UHeight (1) = Height (1) + (MaxHeightBest(1) – Height(1)) * random “ MaxHeightBest is the maximum height observed between all the best individual”
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A New Model for Credit Approval Problems: A Neuro-Genetic System with Quantum Inspiration and Binary-Real Representation
Benchmark Case ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A New Model for Credit Approval Problems: A Neuro-Genetic System with Quantum Inspiration and Binary-Real Representation
Results ,[object Object],nh numQuantum numClassic numGeneration C-Crossover  Q-Crossover minGeneration updatesGeneration
Results ,[object Object],nh 20 numQuantum 2 numClassic 400 numGeneration 200 C-Crossover 0,95  0,020*pi Q-Crossover 0,95 minGeneration 3 updatesGeneration 10
Results ,[object Object],Australian Credit Approval
Results ,[object Object],[object Object]
Results ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A New Model for Credit Approval Problems: A Neuro-Genetic System with Quantum Inspiration and Binary-Real Representation
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ Thanks! Obrigado!” André Vargas, Anderson Pinho and Marley Vellasco “ It is more by education than by instruction that we can transform the humanity ”  Alan Kardec

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Credit Approval Model Using Quantum Neural Network

  • 1. Rio de Janeiro, 11 de Dezembro de 2009. A New Model for Credit Approval Problems: A Neuro-Genetic System with Quantum Inspiration and Binary-Real Representation Anderson Pinho, Marley Vellasco e André Vargas
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  • 33. “ Thanks! Obrigado!” André Vargas, Anderson Pinho and Marley Vellasco “ It is more by education than by instruction that we can transform the humanity ” Alan Kardec