Case-based reasoning: a research paradigm
AI Magazine
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Data mining: concepts and techniques
Data mining: concepts and techniques
A case-based approach using inductive indexing for corporate bond rating
Decision Support Systems - Decision-making and E-commerce systems
Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting
Applied Intelligence
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Expert Systems with Applications: An International Journal
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
A self tuning model for risk estimation
Expert Systems with Applications: An International Journal
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors
Expert Systems with Applications: An International Journal
Business failure prediction using hybrid2 case-based reasoning (H2CBR)
Computers and Operations Research
Application of a 3NN+1 based CBR system to segmentation of the notebook computers market
Expert Systems with Applications: An International Journal
Applying case-based reasoning for product configuration in mass customization environments
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An application of case-based reasoning with machine learning for forensic autopsy
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
We propose a case-based reasoning (CBR) model that uses preference theory functions for similarity measurements between cases. As it is hard to select the right preference function for every feature and set the appropriate parameters, a genetic algorithm is used for choosing the right preference functions, or more precisely, for setting the parameters of each preference function, as to set attribute weights. The proposed model is compared to the well-known k-nearest neighbour (k-NN) model based on the Euclidean distance measure. It has been evaluated on three different benchmark datasets, while its accuracy has been measured with 10-fold cross-validation test. The experimental results show that the proposed approach can, in some cases, outperform the traditional k-NN classifier.