Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Communications of the ACM
A comparison of discriminant procedures for binary variables
Computational Statistics & Data Analysis
Information Retrieval
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A comparative assessment of classification methods
Decision Support Systems
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
A principled approach for building and evaluating neural network classification models
Decision Support Systems
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Feature subset selection bias for classification learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Competing on Analytics: The New Science of Winning
Competing on Analytics: The New Science of Winning
IEEE Transactions on Knowledge and Data Engineering
Toward a successful CRM: variable selection, sampling, and ensemble
Decision Support Systems
Data mining for credit card fraud: A comparative study
Decision Support Systems
Considerations of sample and feature size
IEEE Transactions on Information Theory
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Classification analysis utilizes features for separating observations into distinct groups for decision-making purposes. This study provides a systematic design for comparing the performance of six classification methods using Monte Carlo simulations and illustrates that the variable selection process is integral in comparing methodologies to ensure minimal bias, enhanced stability, and optimize performance. We quantify the variable selection bias and show that, for sufficiently large samples, this bias is minimized so that methods can be compared. We address topics relevant to model building and provide prescriptions for future comparisons so as to build a body of evidence for recommending their use.