Title :
Neural Network Model for Classification Algorithms and Its Application
Author_Institution :
Sch. of Finance, Zhejiang Univ. of Finance & Econ., Hangzhou, China
Abstract :
Neural network-based systems that allow the system, through an analysis of historical data, to determine the relationship between account characteristics and the probability of default. The backpropagation algorithm - the multilayer feedforward network structure is described based on data with nine financial ratios from 81 firms listed in China. A simulation on network is made. Neural network model for classification algorithms are established. By varying network parameters we demonstrate that LevenbergMarque training error is smallest among 4 learning algorithms and its performance is better. Increasing the number of hidden layer can result in minor improvement.
Keywords :
backpropagation; feedforward neural nets; finance; pattern classification; probability; LevenbergMarque training error; backpropagation algorithm; classification algorithm; financial ratio; learning algorithm; multilayer feedforward network; neural network model; Artificial neural networks; Backpropagation algorithms; Classification algorithms; Classification tree analysis; Feedforward neural networks; Finance; Mathematical model; Multi-layer neural network; Neural networks; Predictive models; Classification Algorithms; Financial Ratio; Neural Network; The Multilayer Feedforward Network;
Conference_Titel :
Control Conference, 2006. CCC 2006. Chinese
Conference_Location :
Harbin
Print_ISBN :
7-81077-802-1
DOI :
10.1109/CHICC.2006.280600
