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Developing Classification Techniques from Biological Databases Using Simulated Annealing

  • B. de la Iglesia
  • J. J. Wesselink
  • V. J. Rayward-Smith
  • J. Dicks
  • I. N. Roberts
  • V. Robert
  • T. Boekhout
Part of the Applied Optimization book series (APOP, volume 86)

Abstract

This paper describes new approaches to classification/identification of biological data. It is expected that the work may be extensible to other domains such as the medical domain or fault diagnostic problems. Organisms are often classified according to the value of tests which are used for measuring some characteristic of the organism. When selecting a suitable test set it is important to choose one of minimum cost. Equally, when classification models are constructed for the posterior identification of unnamed individuals it is important to produce optimal models in terms of identification performance and cost. In this paper, we first describe the problem of selecting an economic test set for classification. We develop a criterion for differentiation of organisms which may encompass fuzzy differentiability. Then, we describe the problem of using batches of tests sequentially for identification of unknown organisms, and we explore the problem of constructing the best sequence of batches of tests in terms of cost and identification performance. We discuss how metaheuristic algorithms may be used in the solution of these problems. We also present an application of the above to the problem of yeast classification and identification.

Keywords

Classification Identification Minimum test set (MTS) Heuristic techniques. 

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • B. de la Iglesia
    • 1
  • J. J. Wesselink
    • 1
  • V. J. Rayward-Smith
    • 1
  • J. Dicks
    • 2
  • I. N. Roberts
    • 3
  • V. Robert
    • 4
  • T. Boekhout
    • 4
  1. 1.School of Information SystemsUniversity of East AngliaNorwichEngland
  2. 2.John Innes CentreNorwich Research Park ColneyNorwichEngland
  3. 3.Institute of Food ResearchNorwich Research Park ColneyNorwichEngland
  4. 4.Centraalbureau voor SchimmelculturesUtrechtThe Netherlands

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