Matches in SemOpenAlex for { <https://semopenalex.org/work/W2142732330> ?p ?o ?g. }
Showing items 1 to 78 of
78
with 100 items per page.
- W2142732330 abstract "C-regression models are known as very useful tools in many fields. Since now, many trials to construct c-regression models for data with uncertainty in independent and dependent variables have been done. However, there are few c-regression models for data with uncertainty in independent variables in comparison with dependent variables now. The reason is as follows. The models are constructed using optimal solutions which is derived by solving an optimization problem “analytically”. The problem for data with uncertainty in dependent variables can be easily solved but it is very difficult to solve the problem for data with uncertainty in independent variables “analytically”. Therefore, most of the models for data with uncertainty in independent variables are constructed in which the solutions are calculated “numerically”. By the way, we have proposed “tolerance” of a convenient tool to handle data with uncertainty and applied it to some of clustering algorithms. This concept of tolerance is very useful. The reason is that we can handle data with uncertainty in the framework of optimization to use the concept, without introducing some particular measure between intervals. Especially when we handle the data with missing values of its attributes in the framework of optimization like as fuzzy c-means clustering, this tool is effective. Besides, we think that the tolerance is also available when we consider to construct a regression model for data with uncertainty in independent and dependent variables. In this paper, we first derive the optimal solutions for c-regression models for data with uncertainty in independent and dependent variables “analytically” by using the concept of tolerance. Second, we construct hard and fuzzy c-regression models for data with tolerance in independent and dependent variables. Moreover, we estimate effectiveness of the algorithms through some numerical examples." @default.
- W2142732330 created "2016-06-24" @default.
- W2142732330 creator A5022010621 @default.
- W2142732330 creator A5057153302 @default.
- W2142732330 creator A5060898444 @default.
- W2142732330 creator A5079605823 @default.
- W2142732330 date "2010-07-01" @default.
- W2142732330 modified "2023-09-25" @default.
- W2142732330 title "Hard and fuzzy c-regression models for data with tolerance in independent and dependent variables" @default.
- W2142732330 cites W1513271081 @default.
- W2142732330 cites W2008655746 @default.
- W2142732330 cites W2029389322 @default.
- W2142732330 cites W2108457651 @default.
- W2142732330 cites W2113076747 @default.
- W2142732330 cites W53698588 @default.
- W2142732330 doi "https://doi.org/10.1109/fuzzy.2010.5584335" @default.
- W2142732330 hasPublicationYear "2010" @default.
- W2142732330 type Work @default.
- W2142732330 sameAs 2142732330 @default.
- W2142732330 citedByCount "4" @default.
- W2142732330 countsByYear W21427323302014 @default.
- W2142732330 countsByYear W21427323302017 @default.
- W2142732330 crossrefType "proceedings-article" @default.
- W2142732330 hasAuthorship W2142732330A5022010621 @default.
- W2142732330 hasAuthorship W2142732330A5057153302 @default.
- W2142732330 hasAuthorship W2142732330A5060898444 @default.
- W2142732330 hasAuthorship W2142732330A5079605823 @default.
- W2142732330 hasConcept C105795698 @default.
- W2142732330 hasConcept C119857082 @default.
- W2142732330 hasConcept C124101348 @default.
- W2142732330 hasConcept C126255220 @default.
- W2142732330 hasConcept C152877465 @default.
- W2142732330 hasConcept C154945302 @default.
- W2142732330 hasConcept C27574286 @default.
- W2142732330 hasConcept C33923547 @default.
- W2142732330 hasConcept C41008148 @default.
- W2142732330 hasConcept C58166 @default.
- W2142732330 hasConcept C73555534 @default.
- W2142732330 hasConcept C83546350 @default.
- W2142732330 hasConceptScore W2142732330C105795698 @default.
- W2142732330 hasConceptScore W2142732330C119857082 @default.
- W2142732330 hasConceptScore W2142732330C124101348 @default.
- W2142732330 hasConceptScore W2142732330C126255220 @default.
- W2142732330 hasConceptScore W2142732330C152877465 @default.
- W2142732330 hasConceptScore W2142732330C154945302 @default.
- W2142732330 hasConceptScore W2142732330C27574286 @default.
- W2142732330 hasConceptScore W2142732330C33923547 @default.
- W2142732330 hasConceptScore W2142732330C41008148 @default.
- W2142732330 hasConceptScore W2142732330C58166 @default.
- W2142732330 hasConceptScore W2142732330C73555534 @default.
- W2142732330 hasConceptScore W2142732330C83546350 @default.
- W2142732330 hasLocation W21427323301 @default.
- W2142732330 hasOpenAccess W2142732330 @default.
- W2142732330 hasPrimaryLocation W21427323301 @default.
- W2142732330 hasRelatedWork W1543353791 @default.
- W2142732330 hasRelatedWork W1648231765 @default.
- W2142732330 hasRelatedWork W1967940267 @default.
- W2142732330 hasRelatedWork W1970721789 @default.
- W2142732330 hasRelatedWork W1989970896 @default.
- W2142732330 hasRelatedWork W1994389660 @default.
- W2142732330 hasRelatedWork W2051477907 @default.
- W2142732330 hasRelatedWork W2067200094 @default.
- W2142732330 hasRelatedWork W2093001451 @default.
- W2142732330 hasRelatedWork W2104507918 @default.
- W2142732330 hasRelatedWork W2122167797 @default.
- W2142732330 hasRelatedWork W2135525924 @default.
- W2142732330 hasRelatedWork W2169986380 @default.
- W2142732330 hasRelatedWork W2767841556 @default.
- W2142732330 hasRelatedWork W3025718144 @default.
- W2142732330 hasRelatedWork W3138095899 @default.
- W2142732330 hasRelatedWork W3202500181 @default.
- W2142732330 hasRelatedWork W1999058847 @default.
- W2142732330 hasRelatedWork W2143517607 @default.
- W2142732330 hasRelatedWork W2161025652 @default.
- W2142732330 isParatext "false" @default.
- W2142732330 isRetracted "false" @default.
- W2142732330 magId "2142732330" @default.
- W2142732330 workType "article" @default.