A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Genetic Algorithms and Machine Learning
Machine Learning
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
Empirical analysis of support vector machine ensemble classifiers
Expert Systems with Applications: An International Journal
Consumer credit scoring models with limited data
Expert Systems with Applications: An International Journal
The consumer loan default predicting model - An application of DEA-DA and neural network
Expert Systems with Applications: An International Journal
Quasi-random initial population for genetic algorithms
Computers & Mathematics with Applications
On preprocessing data for financial credit risk evaluation
Expert Systems with Applications: An International Journal
Genetic algorithms for modelling and optimisation
Journal of Computational and Applied Mathematics - Special issue: Mathematics applied to immunology
Expert Systems with Applications: An International Journal
An improved genetic algorithm for optimal feature subset selection from multi-character feature set
Expert Systems with Applications: An International Journal
An effective genetic algorithm for the flexible job-shop scheduling problem
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Attribute selection method based on a hybrid BPNN and PSO algorithms
Applied Soft Computing
Expert Systems with Applications: An International Journal
Global geometric similarity scheme for feature selection in fault diagnosis
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, an advanced novel heuristic algorithm is presented, the hybrid genetic algorithm with neural networks (HGA-NN), which is used to identify an optimum feature subset and to increase the classification accuracy and scalability in credit risk assessment. This algorithm is based on the following basic hypothesis: the high-dimensional input feature space can be preliminarily restricted to only the important features. In this preliminary restriction, fast algorithms for feature ranking and earlier experience are used. Additionally, enhancements are made in the creation of the initial population, as well as by introducing an incremental stage in the genetic algorithm. The performances of the proposed HGA-NN classifier are evaluated using a real-world credit dataset that is collected at a Croatian bank, and the findings are further validated on another real-world credit dataset that is selected in a UCI database. The classification accuracy is compared with that presented in the literature. Experimental results that were achieved using the proposed novel HGA-NN classifier are promising for feature selection and classification in retail credit risk assessment and indicate that the HGA-NN classifier is a promising addition to existing data mining techniques.