Result: Support vector machines approach to pattern detection in bankruptcy prediction and its contingency

Title:
Support vector machines approach to pattern detection in bankruptcy prediction and its contingency
Authors:
SHIN, Kyung-Shik, KYOUNG JUN LEE, KIM, Hyun-Jung
Source:
Neural information processing (Calcutta, 22-25 November 2004)Lecture notes in computer science. :1254-1259
Publisher Information:
Berlin: Springer, 2004.
Publication Year:
2004
Physical Description:
print, 10 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Approximation fonction, Function approximation, Détection forme, Shape detection, Detección forma, Prédiction, Prediction, Predicción, Réseau neuronal, Neural network, Red neuronal, Solution optimale, Optimal solution, Solución óptima, Faillite, Bankruptcy, Quiebra, Machine exemple support, Vector support machine, Máquina ejemplo soporte
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Ewha omans University, College of Business Administration, 11-1 Daehyun-Dong, Seodaemun-Gu, Seoul 120-750, Korea, Republic of
School of Business, Kyung Hee University, Hoegi-Dong, Dongdaemun-Ku, Seoul, Korea, Republic of
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16442873
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.16442873
Database:
PASCAL Archive

Further Information

This study investigates the effectiveness of support vector machines (SVM) approach in detecting the underlying data pattern for the corporate failure prediction tasks. Back-propagation neural network (BPN) has some limitations in that it needs a modeling art to find an appropriate structure and optimal solution and also large training set enough to search the weights of the network. SVM extracts the optimal solution with the small training set by capturing geometric characteristics of feature space without deriving weights of networks from the training data. In this study, we show the advantage of SVM approach over BPN to the problem of corporate bankruptcy prediction. SVM shows the highest level of accuracies and better generalization performance than BPN especially when the training set size is smaller.