The impact of preprocessing on data mining: an evaluation of classifier sensitivity in direct marketing

DOI10.1016/j.ejor.2005.07.023zbMath1120.90349OpenAlexW2140898374MaRDI QIDQ2497261

Stefan Lessmann, Sven F. Crone, Robert Stahlbock

Publication date: 4 August 2006

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2005.07.023

zbMATH Keywords

classificationneural networksmarketingdata miningdata preprocessing

Mathematics Subject Classification ID

Related Items (10)

Behavior-aware user response modeling in social media: learning from diverse heterogeneous data ⋮ Quantitative models for direct marketing: A review from systems perspective ⋮ Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research ⋮ A mixed integer linear programming support vector machine for cost-effective group feature selection: branch-cut-and-price approach ⋮ Solving dimension reduction problems for classification using promoted crow search algorithm (PCSA) ⋮ Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms ⋮ A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data ⋮ Synergies of operations research and data mining ⋮ Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach ⋮ A reference model for customer-centric data mining with support vector machines

Uses Software

• LIBSVM
• C4.5
• SMOTE

Cites Work

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• Neural networks in business: Techniques and applications for the operations researcher
• A bibliography of neural network business applications research: 1994--1998
• Neural network credit scoring models
• Bayesian neural network learning for repeat purchase modelling in direct marketing
• Principal component analysis.
• Optimization-based feature selection with adaptive instance sampling
• Knowledge discovery in a direct marketing case using least squares support vector machines
• Benchmarking state-of-the-art classification algorithms for credit scoring
• A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms

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