A GA-Based Support Vector Machine Diagnosis Model for Business Crisis

被引：0

作者：

Yang, Ming-Fen ^{[1
]}

Hsiao, Huey-Der ^{[2
]}

机构：

[1] Far East Univ, Dept Leisure & Sports Management, Tainan, Taiwan

[2] Far East Univ, Dept Business Adm, Tainan, Taiwan

来源：

COMPUTATIONAL COLLECTIVE INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, PT I | 2010年 / 6421卷

关键词：

Business crisis; Diagnosis model; Genetic algorithm; Support vector machine; DISCRIMINANT-ANALYSIS; NEURAL-NETWORKS; PREDICTION; FAILURE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This research proposes a diagnosis model for business crisis integrated a real-valued genetic algorithm and support vector machine. A series of learning and testing processes with real business data show that the diagnosis model has a crisis prediction accuracy of up to 95.56%, demonstrating the applicability of the proposed method. Six features, including five financial and one intellectual capital indices, are used for the diagnosis. These features are common and easily accessible from publicly available information. The proposed GA-SVM diagnosis model can be used by firms for self-diagnosis and evaluation.

引用

页码：265 / +

页数：3

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