… to rough sets. In Section 3, we discuss sample pair selection with rough set and study its
properties… In Section 4, we develop algorithms to compute one sample pair selection and finding …

… sample selection is first studied to classify each incoming sample as a useless or useful
sample … The useless samples do not contribute a bit to the incremental computation of attribute …

… applications of rough set methods for feature selection in … basic constructs of rough set
approach in feature selection, namely … selection we discuss feature selection criteria, including the …

… a sample space into disjoint equivalence classes or sample … the sample space. In this
section, to lay the foundation for our theorem and proof, some basic concepts related to rough sets …

… Sample selection based on rough sets In this section we introduce the definition of
sample selection based on rough sets. For every conditional attribute we define its relative …

… In Section II, we review some basic concepts related to classical fuzzy rough sets and
introduce the rough approximation of the fuzzy decision of samples. In Section III, we develop a …

… be described as a decision system such that D S = < U l ∪ U u , A T ∪ { d } > , in which U l
is a nonempty and finite set of labeled samples, U u is a nonempty and finite set of unlabeled …

… sampling is a simple technique used in all three sampling … a feature selection method based
on neighborhood rough sets … using a dataset after feature selection. The classification errors …

… which is using rough set theory with greedy heuristics for feature selection. Selecting features
… We would like to use the sample database shown in Table 1 as an example to explain how …

… feature selection. To solve this problem, this paper proposes a robust … rough set model
(RS-FRS) based on representative samples. Firstly, the fuzzy membership degree of the samples …