In streamwise feature selection, new features are sequentially considered for addition to a predic- tive model. When the space of potential features is large, streamwise feature selection offers many advantages over traditional feature selection methods, which assume that all features are known in advance.
Sep 19, 2016 · Streamwise feature selection (SFS) is the task of selecting a best feature subset in SF scenarios. Any SFS method must satisfy three critical ...
Oct 22, 2024 · The proposed method uses the significance analysis concepts in RS theory to control the unknown feature space in SFS problems. This algorithm is ...
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For multi-criteria decision making (MCDM), rough sets are used to obtain decision rules by reducing attributes and objects. However, different reduction methods ...
In this paper, the OSFS problem is considered from the rough sets (RS) perspective and a new OSFS algorithm, called OS-NRRSAR-SA, is proposed.
In streamwise feature selection, new features are sequentially considered for addition to a predictive model. When the space of potential features is large, ...
This paper presents a rough sets-based online feature selection algorithm for OSF. The proposed method, which is called OSFS-NRFS, consists of two major steps: ...
Rough Set Theory, proposed by Pawlak, has been proven to be an effective tool for feature selection, rule extraction, and knowledge discovery [17]. One of the ...
A novel feature selection algorithm that considers feature interaction based on neighborhood rough set ... Streamwise feature selection: a rough set method · M.
Rough set theory is widely used as an effective tool for feature selection, specifically the neighborhood rough set. However, the two main neighborhood ...