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

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.