A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy

Wu, Chih-Hung; Tzeng, Gwo‐Hshiung; Goo, Yeong-Jia; Fang, Wenchang

doi:10.1016/j.eswa.2005.12.008

Cited by 323 publications

(144 citation statements)

References 34 publications

Supporting

Mentioning

139

Contrasting

Unclassified

Order By: Relevance

“…? 87.27% Wilson and Sharda [41] 98.00% 97.50% 97.75% Wu et al [77] 99.40% 83.30% Yang and Harrison [83] 95.50% 92.37% 95.27% Yang et al [79] 90.00% 63.00% 84.00% Yim and Mitchell [80] 94.00% 80.00% 90.96% Yim and Mitchell [81] 100.00% 89.00% 96.55% ? : Results not mentioned The results presented in this table correspond to the best results achieved with a neural network when many results were presented.…”

Section: Mlp-bpmentioning

confidence: 99%

“…Tan and Dihardjo [73] Variables used in two previous studies MLP-BP Tung et al [74] Correlation test applied to variables used in previous studies MLP-BP -GSOFNN Tyree and Long [33] Method that fits a neural network (sensitivity measure) and stepwise search with a criterion optimized for discriminant analysis MLP-BP -PNN Vieira et al [75] Variables used in previous studies HLVQ -G-Prop Wallrafen et al [74] Genetic algorithm applied to variables that belong to the following financial categories: liquidity, profitability, asset, solidity, debt and reimbursement ability MLP-GA West et al [76] Variables used in study by Altman [2] MLP-BP Wilson and Sharda [41] Variables used in study by Altman [2] MLP-BP Wu et al [77] Variables used in previous studies MLP-BP Yang and Harrison [78] Stepwise search with a criterion optimized for discriminant analysis applied to variables commonly used in financial analysis…”

Section: Mlp-bpmentioning

confidence: 99%

See 1 more Smart Citation

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Jardin

2010

Neurocomputing

106

View full text Add to dashboard Cite

We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction.

show abstract

Section: Mlp-bpmentioning

confidence: 99%

Section: Mlp-bpmentioning

confidence: 99%

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Jardin

2010

Neurocomputing

106

View full text Add to dashboard Cite

show abstract

“…They have been used in a number of applications in engineering and social science. Recently, they applied for optimization of the parameters of support vector machine for predicting bankruptcy [75], and hybrid with neural net work for detecting credit card fraud with high accuracy [31], and have been used along with Artificial Immune System for reducing a number of false alarm in credit card fraud detection. GA has been used in credit card fraud detection for minimizing the wrongly classified number of transactions [17].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria

Zareapoor¹,

R²,

Alam³

2012

IJCA

View full text Add to dashboard Cite

Financial fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The companies and financial institution loose huge amounts due to fraud and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for all credit card issuing banks to minimize their losses. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This paper presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria.

show abstract

“…The parameter γ determines the bandwidth of the RBF kernel (Wu et al, 2007). For optimizing the two SVM parameters (C and γ) simultaneously, a heuristic algorithm, GA, was used.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Application of biomonitoring and support vector machine in water quality assessment

Liao

Jian-yu

Wang

2012

J. Zhejiang Univ. Sci. B

View full text Add to dashboard Cite

Abstract:The behavior of schools of zebrafish (Danio rerio) was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine (SVM) was developed. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration (LC 50 ) of a pollutant. The data were used to develop a method to evaluate water quality, so as to give an early indication of toxicity. Four kinds of metal ions (Cu ) were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm (GA) was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.

show abstract

A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy

Cited by 323 publications

References 34 publications

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria

Application of biomonitoring and support vector machine in water quality assessment

Contact Info

Product

Resources

About