A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Predicting 1-year outcome following acute myocardial infarction: physicians versus computers
Computers and Biomedical Research
Learning hard concepts through constructive induction: framework and rationale
Computational Intelligence
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
A geometric approach to feature selection
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
From decision tables to expert system shells
Data & Knowledge Engineering
A note on comparing classifiers
Pattern Recognition Letters
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Confirmation-guided discovery of first-order rules with tertius
Machine Learning
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
Inductive Learning for Risk Classification
IEEE Expert: Intelligent Systems and Their Applications
Machine Learning
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Feature Selection in Web Applications By ROC Inflections and Powerset Pruning
SAINT '01 Proceedings of the 2001 Symposium on Applications and the Internet (SAINT 2001)
On classes of functions for which No Free Lunch results hold
Information Processing Letters
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
Learning DNF by decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Constructive induction on decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Complex concept acquisition through directed search and feature caching
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
A scheme for feature construction and a comparison of empirical methods
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Note on optimal selection of independent binary-valued features for pattern recognition (Corresp.)
IEEE Transactions on Information Theory
Neural-network feature selector
IEEE Transactions on Neural Networks
Using the Karhunen-Loe've transformation in the back-propagation training algorithm
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Credit scoring algorithm based on link analysis ranking with support vector machine
Expert Systems with Applications: An International Journal
Credit rating method with heterogeneous information
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Expert Systems with Applications: An International Journal
Genetic algorithm-based heuristic for feature selection in credit risk assessment
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
Financial credit-risk evaluation is among a class of problems known to be semi-structured, where not all variables that are used for decision-making are either known or captured without error. Machine learning has been successfully used for credit-evaluation decisions. However, blindly applying machine learning methods to financial credit risk evaluation data with minimal knowledge of data may not always lead to expected results. We present and evaluate some data and methodological considerations that are taken into account when using machine learning methods for these decisions. Specifically, we consider the effects of preprocessing of credit-risk evaluation data used as input for machine learning methods.