A data mining-constraint satisfaction optimization problem for cost effective classification

DOI10.1016/j.cor.2005.01.023zbMath1113.90136OpenAlexW2032721275MaRDI QIDQ2499142

Parag C. Pendharkar

Publication date: 14 August 2006

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2005.01.023

zbMATH Keywords

classificationheuristicsneural networkssimulated annealingartificial intelligencemedical diagnosisconstraint satisfaction optimizationKnapsack optimization

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Combinatorial optimization (90C27) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (1)

Synergies of operations research and data mining

Uses Software

• Knapsack

Cites Work

• Optimization by Simulated Annealing
• Feature minimization within decision trees
• An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based neural approaches for classification.
• A computational study on the performance of artificial neural networks under changing structural design and data distribution
• Dynamic Programming and Strong Bounds for the 0-1 Knapsack Problem
• Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
• Mathematical Programming for Data Mining: Formulations and Challenges
• Unnamed Item

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