Building contextual classifiers by integrating fuzzy rule based classification technique and k-nn method for credit scoring

Authors:
Arijit Laha
Affiliations:
Institute for Development and Research in Banking Technology, Castle Hills, Hyderabad 500 057, India
Venue:
Advanced Engineering Informatics
Year:
2007

Citing 8
Cited 10

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Self-Organizing Maps

Self-Organizing Maps
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation

Management Science
Hybrid mining approach in the design of credit scoring models

Expert Systems with Applications: An International Journal
Some novel classifiers designed using prototypes extracted by a newscheme based on self-organizing feature map

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Constructing accurate fuzzy classifiers: A new adaptive method for rule-weight specification

International Journal of Knowledge-based and Intelligent Engineering Systems
Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

Expert Systems with Applications: An International Journal
Behavioral assessment of recoverable credit of retailer's customers

Information Sciences: an International Journal
Business intelligence for delinquency risk management via cox regression

PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Hybrid credit ranking intelligent system using expert system and artificial neural networks

Applied Intelligence
An evolution strategy-based multiple kernels multi-criteria programming approach: The case of credit decision making

Decision Support Systems
Supporting user participation design using a fuzzy analytic hierarchy process approach

Engineering Applications of Artificial Intelligence
Credit risk evaluation using neural networks: Emotional versus conventional models

Applied Soft Computing
Dynamic classifier ensemble model for customer classification with imbalanced class distribution

Expert Systems with Applications: An International Journal
Evolution strategy based adaptive Lq penalty support vector machines with Gauss kernel for credit risk analysis

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Credit-risk evaluation is a very challenging and important problem in the domain of financial analysis. Many classification methods have been proposed in the literature to tackle this problem. Statistical and neural network based approaches are among the most popular paradigms. However, most of these methods produce so-called ''hard'' classifiers, those generate decisions without any accompanying confidence measure. In contrast, ''soft'' classifiers, such as those designed using fuzzy set theoretic approach; produce a measure of support for the decision (and also alternative decisions) that provides the analyst with greater insight. In this paper, we propose a method of building credit-scoring models using fuzzy rule based classifiers. First, the rule base is learned from the training data using a SOM based method. Then the fuzzy k-nn rule is incorporated with it to design a contextual classifier that integrates the context information from the training set for more robust and qualitatively better classification. Further, a method of seamlessly integrating business constraints into the model is also demonstrated.