Feature minimization within decision trees
Computational Optimization and Applications
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Artificial Neural Networks
Sequential Decision Models for Expert System Optimization
IEEE Transactions on Knowledge and Data Engineering
Dynamic Programming and Strong Bounds for the 0-1 Knapsack Problem
Management Science
Mathematical Programming for Data Mining: Formulations and Challenges
INFORMS Journal on Computing
Journal of Artificial Intelligence Research
A GRASP method for building classification trees
Expert Systems with Applications: An International Journal
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We propose a data mining-constraint satisfaction optimization problem (DM-CSOP) where it is desired to maximize the number of correct classifications at a lowest possible information acquisition cost. We show that the problem can be formulated as a set of several binary variable knapsack optimization problems, which are solved sequentially. We propose a heuristic hybrid simulated annealing and gradient-descent artificial neural network (ANN) procedure to solve the DM-CSOP. Using a real-world heart disease data set, we show that the proposed hybrid procedure provides a low-cost and high-quality solution when compared to a traditional ANN classification approach.The massive proliferation of very large databases in organizations makes it necessary to design cost effective and efficient data mining systems. This paper proposes a data mining constraint satisfaction optimization problem, which provides a high quality cost effective solution for a binary classification problem.