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

共 50 条

[21] Arteriosclerosis Diagnosis Based on Support Vector Machine
Zhang, Yue
Pan, Junjun
[J]. 2018 5TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2018), 2018, : 138 - 142
[22] Discussion of "GA-Based Support Vector Machine Model for the Prediction of Monthly Reservoir Storage" by Jeiqiong Su, Xuan Wang, Yong Liang, and Bin Chen
Bozorg-Haddad, Omid
Fallah-Mehdipour, E.
Mirzaei-Nodoushan, F.
Marino, M. A.
[J]. JOURNAL OF HYDROLOGIC ENGINEERING, 2015, 20 (02)
[23] A hybrid model for fault diagnosis using model based approaches and support vector machine
Lee, Chang Jun
Lee, Gibaek
Han, Chonghun
Yoon, En Sup
[J]. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN, 2006, 39 (10) : 1085 - 1095
[24] Closure to "GA-Based Support Vector Machine Model for the Prediction of Monthly Reservoir Storage" by Jieqiong Su, Xuan Wang, Yong Liang, and Bin Chen
Wang, Xuan
Su, Jieqiong
Chen, Bin
[J]. JOURNAL OF HYDROLOGIC ENGINEERING, 2015, 20 (02)
[25] FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE
LIU Guanjun LIU Xinmin QIU Jing HU Niaoqing College of Mechatronics Engineering and Automation
[J]. Chinese Journal of Mechanical Engineering, 2007, (05) : 92 - 95
[26] Gas Pressure Regulator Fault Diagnosis Model Based on Support Vector Machine
Zhi Xiaoye
Wang Yahui
An Yung
[J]. PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 2019 - 2022
[27] Design of Power Transformer Fault Diagnosis Model Based on Support Vector Machine
Liu, Tao
Wang, Zhijie
[J]. 2009 INTERNATIONAL SYMPOSIUM ON INTELLIGENT UBIQUITOUS COMPUTING AND EDUCATION, 2009, : 137 - +
[28] Breast Cancer Diagnosis Based on Support Vector Machine
Gao, Shang
Li, Hongmei
[J]. 2012 2ND INTERNATIONAL CONFERENCE ON UNCERTAINTY REASONING AND KNOWLEDGE ENGINEERING (URKE), 2012, : 240 - 243
[29] Fault diagnosis based on support vector machine ensemble
Li, Y
Cai, YZ
Yin, RP
Xu, XM
[J]. PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, 2005, : 3309 - 3314
[30] Intelligent fault diagnosis based on support vector machine
Xia Fangfang
Yuan Long
Zhao Xiucai
He Wenan
Jia Ruisheng
[J]. PROCEEDINGS OF 2015 IEEE 12TH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS (ICEMI), VOL. 1, 2015, : 201 - 205

← 1 2 3 4 5 →