Selecting bankruptcy predictors using a Support Vector Machine approach

被引：41

作者：

Fan, A ^{[1
]}

Palaniswami, M ^{[1
]}

机构：

[1] Univ Melbourne, Dept EEE, Melbourne, Vic 3010, Australia

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL VI | 2000年

关键词：

D O I：

10.1109/IJCNN.2000.859421

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Conventional Neural Network approach has been found useful in predicting corporate distress from financial statements. In this paper, we have adopted a Support Vector Machine approach to the problem. A new way of selecting bankruptcy predictors is shown, using the Euclidean distance based criterion calculated within the SVM kernel. A comparative study is provided using three classical corporate distress models and an alternative model based on the SVM approach.

引用

页码：354 / 359

页数：6

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