Conclusion
At a conceptual level, one can divide the task of concept learning into the subtask of selecting a proper subset of features to use in describing the concept, and learning a hypothesis based on these features. This directly leads to a modular design of the learning algorithm which allows flexible combinations of explicit feature selection methods with model induction algorithms and sometimes leads to powerful variants. Many recent works, however, tend to take a more general view of feature selection as part of model selection and therefore integrate feature selection more closely into the learning algorithms (i.e. the Bayesian feature selection methods). Feature selection for clustering is a largely untouched problem, and there has been little theoretical characterization of the heuristic approaches we described in the chapter. In summary, although no universal strategy can be prescribed, for high-dimensional problems frequently encountered in microarray analysis, feature selection offers a promising suite of techniques to improve interpretability, performance and computation efficiency in learning.
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Xing, E.P. (2003). Feature Selection in Microarray Analysis. In: Berrar, D.P., Dubitzky, W., Granzow, M. (eds) A Practical Approach to Microarray Data Analysis. Springer, Boston, MA. https://doi.org/10.1007/0-306-47815-3_6
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