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

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Abstract

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.

Original languageEnglish (US)
Pages (from-to)3124-3135
Number of pages12
JournalComputers and Operations Research
Volume33
Issue number11
DOIs
StatePublished - Nov 1 2006

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research

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