Matches in SemOpenAlex for { <https://semopenalex.org/work/W2029307344> ?p ?o ?g. }
- W2029307344 endingPage "12" @default.
- W2029307344 startingPage "1" @default.
- W2029307344 abstract "A rough set theory is a new mathematical tool to deal with uncertainty and vagueness of decision system and it has been applied successfully in all the fields. It is used to identify the reduct set of the set of all attributes of the decision system. The reduct set is used as preprocessing technique for classification of the decision system in order to bring out the potential patterns or association rules or knowledge through data mining techniques. Several researchers have contributed variety of algorithms for computing the reduct sets by considering different cases like inconsistency, missing attribute values and multiple decision attributes of the decision system. This paper focuses on the review of the techniques for dimensionality reduction under rough set theory environment. Further, the rough sets hybridization with fuzzy sets, neural network and metaheuristic algorithms have also been reviewed. The performance analysis of the algorithms has been discussed in connection with the classification." @default.
- W2029307344 created "2016-06-24" @default.
- W2029307344 creator A5007268502 @default.
- W2029307344 creator A5079604211 @default.
- W2029307344 date "2009-01-01" @default.
- W2029307344 modified "2023-10-18" @default.
- W2029307344 title "Dimensionality reduction based on rough set theory: A review" @default.
- W2029307344 cites W1492891452 @default.
- W2029307344 cites W1508383841 @default.
- W2029307344 cites W1517515497 @default.
- W2029307344 cites W1593284219 @default.
- W2029307344 cites W1892848734 @default.
- W2029307344 cites W1963638628 @default.
- W2029307344 cites W1965388297 @default.
- W2029307344 cites W1974088341 @default.
- W2029307344 cites W1975980892 @default.
- W2029307344 cites W1976034104 @default.
- W2029307344 cites W1977674593 @default.
- W2029307344 cites W1979812372 @default.
- W2029307344 cites W1982907249 @default.
- W2029307344 cites W1983955072 @default.
- W2029307344 cites W1984663816 @default.
- W2029307344 cites W1997605822 @default.
- W2029307344 cites W2001083091 @default.
- W2029307344 cites W2004410614 @default.
- W2029307344 cites W2004728080 @default.
- W2029307344 cites W2011487750 @default.
- W2029307344 cites W2015750786 @default.
- W2029307344 cites W2018631819 @default.
- W2029307344 cites W2019754927 @default.
- W2029307344 cites W2021990760 @default.
- W2029307344 cites W2024638676 @default.
- W2029307344 cites W2027260346 @default.
- W2029307344 cites W2028973107 @default.
- W2029307344 cites W2039438466 @default.
- W2029307344 cites W2040480791 @default.
- W2029307344 cites W2050720134 @default.
- W2029307344 cites W2058879468 @default.
- W2029307344 cites W2064174252 @default.
- W2029307344 cites W2076012609 @default.
- W2029307344 cites W2088997887 @default.
- W2029307344 cites W2089137303 @default.
- W2029307344 cites W2098021177 @default.
- W2029307344 cites W2106751840 @default.
- W2029307344 cites W2108268240 @default.
- W2029307344 cites W2117100923 @default.
- W2029307344 cites W2123509054 @default.
- W2029307344 cites W2126266130 @default.
- W2029307344 cites W2128084896 @default.
- W2029307344 cites W2136206988 @default.
- W2029307344 cites W2142897102 @default.
- W2029307344 cites W2157046433 @default.
- W2029307344 cites W2162364423 @default.
- W2029307344 cites W2165022828 @default.
- W2029307344 cites W2168523997 @default.
- W2029307344 cites W2169585345 @default.
- W2029307344 cites W4255833381 @default.
- W2029307344 doi "https://doi.org/10.1016/j.asoc.2008.05.006" @default.
- W2029307344 hasPublicationYear "2009" @default.
- W2029307344 type Work @default.
- W2029307344 sameAs 2029307344 @default.
- W2029307344 citedByCount "313" @default.
- W2029307344 countsByYear W20293073442012 @default.
- W2029307344 countsByYear W20293073442013 @default.
- W2029307344 countsByYear W20293073442014 @default.
- W2029307344 countsByYear W20293073442015 @default.
- W2029307344 countsByYear W20293073442016 @default.
- W2029307344 countsByYear W20293073442017 @default.
- W2029307344 countsByYear W20293073442018 @default.
- W2029307344 countsByYear W20293073442019 @default.
- W2029307344 countsByYear W20293073442020 @default.
- W2029307344 countsByYear W20293073442021 @default.
- W2029307344 countsByYear W20293073442022 @default.
- W2029307344 countsByYear W20293073442023 @default.
- W2029307344 crossrefType "journal-article" @default.
- W2029307344 hasAuthorship W2029307344A5007268502 @default.
- W2029307344 hasAuthorship W2029307344A5079604211 @default.
- W2029307344 hasConcept C111012933 @default.
- W2029307344 hasConcept C111335779 @default.
- W2029307344 hasConcept C120567893 @default.
- W2029307344 hasConcept C124101348 @default.
- W2029307344 hasConcept C153046414 @default.
- W2029307344 hasConcept C154945302 @default.
- W2029307344 hasConcept C172967692 @default.
- W2029307344 hasConcept C177264268 @default.
- W2029307344 hasConcept C199360897 @default.
- W2029307344 hasConcept C2524010 @default.
- W2029307344 hasConcept C2776825360 @default.
- W2029307344 hasConcept C33923547 @default.
- W2029307344 hasConcept C39105242 @default.
- W2029307344 hasConcept C41008148 @default.
- W2029307344 hasConcept C42011625 @default.
- W2029307344 hasConcept C58166 @default.
- W2029307344 hasConcept C69177213 @default.
- W2029307344 hasConcept C70518039 @default.
- W2029307344 hasConceptScore W2029307344C111012933 @default.
- W2029307344 hasConceptScore W2029307344C111335779 @default.
- W2029307344 hasConceptScore W2029307344C120567893 @default.