A case-based reasoning model that uses preference theory functions for credit scoring

The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in Construction of Decision Tree Models for Credit Scoring

“…Artificial neural networks (ANNs) [5], naive Bayes, logistic regression(LR), recursive partitioning, ANN and sequential minimal optimization (SMO) [6], neural networks (Multilayer feed-forward networks) [7], ANN with standard feed-forward network [8], credit scoring model based on data envelopment analysis (DEA) [9], back propagation ANN [10], link analysis ranking with support vector machine (SVM) [11], SVM [12], integrating non-linear graph-based dimensionality reduction schemes via SVMs [13], Predictive modelling through clustering launched classification and SVMs [14], optimization of k-nearest neighbor (KNN) by GA [15], Evolutionary-based feature selection approaches [16], comparisons between data mining techniques (KNN, LR, discriminant analysis, naive Bayes, ANN and decision trees) [17], SVM [18], intelligent-agent-based fuzzy group decision making model [19], SVMs with direct search for parameters selection [20], SVM [21], decision support system (DSS) using fuzzy TOPSIS [22], neighbourhood rough set and SVM based classifier [23], Bayesian latent variable model with classification regression tree [24], integrating SVM and sampling method in order to computational time reduction for credit scoring [25], use of preference theory functions in case based reasoning model for credit scoring [26], fuzzy probabilistic rough set model [27], using rough set and scatter search met heuristic in feature selection for credit scoring [28], neural networks for credit scoring models in microfinance industry [29].…”

Evaluation System Based on Neural Network Model

“…The researches in foreign countries usually use more cases which is based on statistical category. And more is used in the banking business on the evaluation of the customers to the bank [4,5]. Aiming at the present situation of that domestic evaluation appearing random evaluation and the students' malicious evaluation, and use simple method to compute the average of the scores for teachers.…”

Credit Risk Prediction Using Fuzzy Immune Learning

“…In these situations banks can supervise the existing loans much easier than before [3]. Because of the fast growth of autofinancing in the last two decades, the use of data mining for credit risk prediction increases rapidly [4][5][6][7]. The first investigation into credit scoring was started by Olson and Wu in 2010 to classify credit applications as good or bad payers [8].…”