The k-feature set problem is W[2]-complete
Journal of Computer and System Sciences - Special issue on Parameterized computation and complexity
IEEE Transactions on Information Technology in Biomedicine
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Feature selection and parameter settings for classifiers are both important issues in computer-assisted medical diagnosis. In the present paper, we highlight some of the complexity problems posed by both tasks. For the feature selection problem we propose a search-based procedure with a proven time bound for the convergence to optimum solutions. Interestingly, the time bound differs from fixed-parameter tractable algorithms by an instance-specific factor only. The stochastic search method has been utilized in the context of micro array data classification. For the classification of medical images we propose a generic upper bound for the size of classifiers that basically depends on the number of training samples only. The evaluation on a number of benchmark problems produced a close correspondence to the size of classifiers with best generalization results reported in the literature.