Online Feature Selection for Streaming Features Using Self-Adaption Sliding-Window Sampling
نویسندگان
چکیده
منابع مشابه
Online Streaming Feature Selection Using Geometric Series of the Adjacency Matrix of Features
Feature Selection (FS) is an important pre-processing step in machine learning and data mining. All the traditional feature selection methods assume that the entire feature space is available from the beginning. However, online streaming features (OSF) are an integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with t...
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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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As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed. Online streaming feature selection provides a new, complementary algorithmic methodology to enrich online feature selection, especially targets to high dimensionality in big data analytics. This paper introduces the first ...
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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.2019.2894121