Amir Daneshvar; Mostafa Zandieh; Jamshid Nazemi
Volume 13, Issue 39 , January 2016, , Pages 1-34
Abstract
Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the ...
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Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the imprecise nature of data, Obtaining all these parameters simultaneously specially in large scale realistic credit problems which requires real time decision making is very complex and time-consuming.Preference Disaggregation approach infers these parameters from the holistic judgements provided by decision maker. This approach within multicriteria decision methods is equivalent to machine learning in artificial intelligence discipline.Under this approach this paper proposes a new learning method in which Genetic Algorithm(GA) in an evolutionary process induces all , ELECTRE TRI model parameters from training set then at the end of this process, classification is done on testing set by inferred parameters. Experimental analysis on credit data shows high quality and competitive results compared with some standard classification methods.
Mohammad Taghi Taghavifard; Ahmad Nadali
Volume 9, Issue 25 , July 2012, , Pages 85-107
Abstract
This research study aims at using Data Mining and Fuzzy Logicapproaches to classify the credit scoring of banking system applicantsas to cover uncertainties and ambiguity connected with applicantclasses and also variables that affect their behavior.The methodology, according to a standard Data Mining ...
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This research study aims at using Data Mining and Fuzzy Logicapproaches to classify the credit scoring of banking system applicantsas to cover uncertainties and ambiguity connected with applicantclasses and also variables that affect their behavior.The methodology, according to a standard Data Mining process, is tocollect and refine the client data, then those variables which are inlinguistic forms are converted to fuzzy variables under the supervisionof banking experts and final data are modeled using Fuzzy DecisionTree, subsequently. The unfuzzy data are also modeled using the otheralgorithms.The results of the study suggest that as far as client distinctionaccuracy is concerned Fuzzy Decision Tree produces better resultscompared to Traditional Trees, Neural Networks, and statisticalprocedures such as Logistic Regression and Bayesian Network.However, it is not as accurate as Support Vector Machine and GeneticTree. On the other hand, Fuzzy Decision Tree technique has gainedbetter prediction than prediction performance of bank credit scoringexperts.
