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- W4295122168 abstract "The amount of information in the form of features and variables avail- able to machine learning algorithms is ever increasing. This can lead to classifiers that are prone to overfitting in high dimensions, high di- mensional models do not lend themselves to interpretable results, and the CPU and memory resources necessary to run on high-dimensional datasets severly limit the applications of the approaches. Variable and feature selection aim to remedy this by finding a subset of features that in some way captures the information provided best. In this paper we present the general methodology and highlight some specific approaches." @default.
- W4295122168 created "2022-09-10" @default.
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- W4295122168 date "2014-02-10" @default.
- W4295122168 modified "2023-09-25" @default.
- W4295122168 title "Feature and Variable Selection in Classification" @default.
- W4295122168 doi "https://doi.org/10.48550/arxiv.1402.2300" @default.
- W4295122168 hasPublicationYear "2014" @default.
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