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By using mathematical logic and the fuzzy technique, we present an approach to the computation of optimal feature sets for diagnostic tests in microbiology. These sets of physiological and biochemical tests are suitable for differentiating all organisms belonging to a given group. Membership functions are defined which are able to reflect the time dependence of the test reaction states. The result is a suboptimal solution characterized by short feature sets of high diagnostic value. An application to Rhodotoruhz species is shown. Classification and diagnostics in microbiology can be performed by means of qualitative feature patterns such as morphological, physiological, biochemical, and sometimes ecological characteristics. These characteristics can be used for differentiating between both genera and species. The solution of a diagnostic problem on the basis of given feature patterns leads to a need for dimension reduction, because good differentiation between individuals is achieved if a subset of features having a high diagnostic value is used (3, 8) * Generally, in pattern recognition the problem of selecting features with high separation power (as a mathematical optimization problem) is solved principally as follows (5): (i) Starting from a given feature set, subsets can be formed which minimize a given goodness criterion by means of search strategies, resulting in a suboptimal solution. (ii) Transformation techniques such as the Karhunen-Loeve transformation (problem-dependent optimal solution) or the Fourier transformation (general method) can be applied. These transformation techniques produce new artificial fea- tures with suitable properties (low dimension, orthogonality, no correlations).

Feature Selection Approach for Diagnosis in Microbiology and Its Application to Yeast Species