Bayesian Penalized Method for Streaming Feature Selection
نویسندگان
چکیده
منابع مشابه
Streaming Feature Selection
When learning predictive models that may require testing hundred of thousands of features in order to nd tens of signi cant features, it is often desirable to interleave feature generation with feature selection. New features (e.g. interaction terms) can then be generated lazily based on which features have already proven signi cant. We address three issues: 1) when feature selection is prefera...
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In the paper, we consider an interesting and challenging problem, online streaming feature selection, in which the size of the feature set is unknown, and not all features are available from learning while leaving the number of observations constant. In this problem, the candidate features arrive one at a time, and the learner's task is to select a “best so far” set of features from streaming f...
متن کاملOnline Streaming Feature Selection
We study an interesting and challenging problem, online streaming feature selection, in which the size of the feature set is unknown, and not all features are available for learning while leaving the number of observations constant. In this problem, the candidate features arrive one at a time, and the learner's task is to select a “best so far” set of features from streaming features. Standard ...
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In Streaming Feature Selection (SFS), new features are sequentially considered for addition to a predictive model. When the space of potential features is large, SFS offers many advantages over methods in which all features are assumed to be known in advance. Features can be generated dynamically, focusing the search for new features on promising subspaces, and overfitting can be controlled by ...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2930346