C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Feature Subset Selection within a Simulated Annealing DataMining Algorithm
Journal of Intelligent Information Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Monotonic Measure for Optimal Feature Selection
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Selective Breeding in a Multiobjective Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
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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.