Definition (or Synopsis)
Feature selection, as a dimensionality reduction technique, aims to choose a small subset of the relevant features from the original ones by removing irrelevant, redundant, or noisy features. Feature selection usually leads to better learning performance, i.e., higher learning accuracy, lower computational cost, and better model interpretability.
Generally speaking, irrelevant features are features that cannot help discriminate samples from different classes(supervised) or clusters(unsupervised). Removing irrelevant features will not affect learning performance. In fact, the removal of irrelevant features may help learn a better model, as irrelevant features may confuse the learning system and cause memory and computation inefficiency. For example, in Fig. 1a, f1 is a relevant feature because f1 can discriminate class1 and class2. In Fig. 1b, f2 is a redundant feature because f2cannot...
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Wang, S., Tang, J., Liu, H. (2017). Feature Selection. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_101
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_101
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