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S. Maldonado, R. Weber (2009)
A wrapper method for feature selection using Support Vector MachinesInf. Sci., 179
S. Konishi (1979)
Asymptotic expansions for the distributions of functions of a correlation matrixJournal of Multivariate Analysis, 9
Xuechuan Wang, K. Paliwal (2003)
Feature extraction and dimensionality reduction algorithms and their applications in vowel recognitionPattern Recognit., 36
Shijin Li, Hao Wu, D. Wan, Jiali Zhu (2011)
An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machineKnowl. Based Syst., 24
Li-Yeh Chuang, Cheng-Hong Yang, Jung-Chike Li (2011)
Chaotic maps based on binary particle swarm optimization for feature selectionAppl. Soft Comput., 11
K. Shin, Yong-Joo Lee (2002)
A genetic algorithm application in bankruptcy prediction modelingExpert Syst. Appl., 23
Douglas Chatfield, Erwin Janek (1972)
Attribute selection in concept identificationJournal of Experimental Psychology, 95
(1980)
The Analytic Hierarchical Process
K. Tan, E. Teoh, Qiang Yu, K. Goh (2009)
A hybrid evolutionary algorithm for attribute selection in data miningExpert Syst. Appl., 36
Yi Peng, Gang Kou, Guoxun Wang, Yong Shi (2011)
FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithmsOmega-international Journal of Management Science, 39
I. Bose (2006)
Deciding the financial health of dot-coms using rough setsInf. Manag., 43
Chih-Fong Tsai (2009)
Feature selection in bankruptcy predictionKnowl. Based Syst., 22
D. Giles, V. Srivastava (1993)
The exact distribution of a least squares regression coefficient estimator after a preliminary t-testStatistics & Probability Letters, 16
P. Ravisankar, V. Ravi, I. Bose (2010)
Failure prediction of dotcom companies using neural network-genetic programming hybridsInf. Sci., 180
Jongsik Yoon, Young Kwon (2010)
A practical approach to bankruptcy prediction for small businesses: Substituting the unavailable financial data for credit card sales informationExpert Syst. Appl., 37
M. Boyacioglu, Y. Kara, Ö. Baykan (2009)
Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in TurkeyExpert Syst. Appl., 36
Chien-Pang Lee, Y. Leu (2011)
A novel hybrid feature selection method for microarray data analysisAppl. Soft Comput., 11
A. U.S. (2003)
Measuring the efficiency of decision making units
(2000)
Multiple attribute decision making methods and applications
R.R. Pagano (2001)
Understanding Statistics in the Behavioral Sciences, Sixth ed
H. Schneeweiß, H. Mathes (1995)
Factor Analysis and Principal ComponentsJournal of Multivariate Analysis, 55
R. Noori, M. Sabahi, A. Karbassi, A. Baghvand, H. Zadeh (2010)
Multivariate statistical analysis of surface water quality based on correlations and variations in the data setDesalination, 260
Yonghong Peng, Z. Wu, Jianmin Jiang (2010)
A novel feature selection approach for biomedical data classificationJournal of biomedical informatics, 43 1
C. Cornelis, Richard Jensen, G. Martín, D. Ślęzak (2010)
Attribute selection with fuzzy decision reductsInf. Sci., 180
B. Helena, B. Helena, R. Pardo, M. Vega, E. Barrado, J. Fernández, L. Fernández (2000)
Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga river, Spain) by Principal Component Analysis.Water Research, 34
(1976)
The DEMATEL Observer
Rajiv Menjoge, R. Welsch (2010)
A diagnostic method for simultaneous feature selection and outlier identification in linear regressionComput. Stat. Data Anal., 54
Sungbin Cho, Hyojung Hong, B. Ha (2010)
A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy predictionExpert Syst. Appl., 37
A. Atiya (2001)
Bankruptcy prediction for credit risk using neural networks: A survey and new resultsIEEE transactions on neural networks, 12 4
Hui Li, Jie Sun (2009)
Forecasting business failure in China using case‐based reasoning with hybrid case respresentationJournal of Forecasting, 29
Attribute Selection is an important issue for developing a prediction model, however, how to determine an effective attribute selection algorithm is an important but difficult issue. Attribute selection can effectively delete the irrelevant and redundant attributes to increase the prediction accuracy, and evaluating attribute selection methods usually need to consider several criteria such as accuracy, type I error, and type II error. In this paper, the selected attribute process is modeled as a group multiple attributes decision making (GMADM) problem. In evaluating different GMACD methods, the most results usually are consistently, But there are some situations where the evaluated outcomes have different results. The GMADM method is useful tool for evaluating attribute selection algorithms, and the TOPSIS is capable of identifying a compromised solution when different GMADM method result in conflicting rankings. Therefore, this paper proposes an objective (persuasive) GMADM-based attributes selection method to solve this disagreement and help decision makers pick the most suitable method. After verification, the proposed model is more persuasive to evaluate the attributes selection methods for developing prediction model.
Quality & Quantity – Springer Journals
Published: May 17, 2012
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