Matches in SemOpenAlex for { <https://semopenalex.org/work/W2803781749> ?p ?o ?g. }
- W2803781749 endingPage "28500" @default.
- W2803781749 startingPage "28488" @default.
- W2803781749 abstract "Learning from imbalanced data is a challenging task in the fields of machine learning and data mining. As an effective and efficient solution, cost-sensitive learning has been widely adopted to address class imbalance learning (CIL) problems. Weighted extreme learning machine (WELM), which is constructed based on ELM, is a significant member in the cost-sensitive-learning algorithmic family. WELM can effectively deal with CIL problems. However, it has two main drawbacks: 1) it has high time complexity on large-scale data since a large-matrix multiplication operation is required in the solution procedure and 2) it lacks flexibility since it can only tune the training error for each instance and not for each class label. In this paper, we present an alternative to WELM, which is called label-WELM (LW-ELM). Unlike WELM, LW-ELM copes with CIL problems by tuning the training error of each class label. Specifically, the expected output (or training class label) that corresponds to the minority class is augmented, thereby providing stronger tolerance to training errors of the minority-class instances. In this paper, we design two types of weight allocation strategies, both of which are based on the class-imbalance ratio (CIR). In contrast with WELM, LW-ELM is fast and flexible, where fast means that it has low-time complexity and flexible indicates that it can also be used to tackle imbalanced multi-label learning problems, while WELM cannot. The experimental results on binary-class, multiclass, and multi-label data sets with skewed class distributions show the effectiveness and superiority of the proposed LW-ELM algorithm." @default.
- W2803781749 created "2018-06-01" @default.
- W2803781749 creator A5012904505 @default.
- W2803781749 creator A5014505938 @default.
- W2803781749 creator A5024632184 @default.
- W2803781749 creator A5030350248 @default.
- W2803781749 creator A5034411104 @default.
- W2803781749 creator A5062619125 @default.
- W2803781749 date "2018-01-01" @default.
- W2803781749 modified "2023-10-17" @default.
- W2803781749 title "LW-ELM: A Fast and Flexible Cost-Sensitive Learning Framework for Classifying Imbalanced Data" @default.
- W2803781749 cites W1045349118 @default.
- W2803781749 cites W1100975233 @default.
- W2803781749 cites W1850407572 @default.
- W2803781749 cites W1894514722 @default.
- W2803781749 cites W1941659294 @default.
- W2803781749 cites W1966748751 @default.
- W2803781749 cites W1966950763 @default.
- W2803781749 cites W1984606274 @default.
- W2803781749 cites W1991181258 @default.
- W2803781749 cites W1993023793 @default.
- W2803781749 cites W2006820260 @default.
- W2803781749 cites W2015452969 @default.
- W2803781749 cites W2022348393 @default.
- W2803781749 cites W2026131661 @default.
- W2803781749 cites W2032867948 @default.
- W2803781749 cites W2033184625 @default.
- W2803781749 cites W2035144760 @default.
- W2803781749 cites W2039163401 @default.
- W2803781749 cites W2040181375 @default.
- W2803781749 cites W2067178084 @default.
- W2803781749 cites W2074888575 @default.
- W2803781749 cites W2076637413 @default.
- W2803781749 cites W2078622091 @default.
- W2803781749 cites W2087787741 @default.
- W2803781749 cites W2094764962 @default.
- W2803781749 cites W2094947835 @default.
- W2803781749 cites W2096235960 @default.
- W2803781749 cites W2096945460 @default.
- W2803781749 cites W2099454382 @default.
- W2803781749 cites W2103614420 @default.
- W2803781749 cites W2107327607 @default.
- W2803781749 cites W2111072639 @default.
- W2803781749 cites W2114315281 @default.
- W2803781749 cites W2118978333 @default.
- W2803781749 cites W2119191234 @default.
- W2803781749 cites W2120457925 @default.
- W2803781749 cites W2121971770 @default.
- W2803781749 cites W2126783427 @default.
- W2803781749 cites W2133223948 @default.
- W2803781749 cites W2135074661 @default.
- W2803781749 cites W2148143831 @default.
- W2803781749 cites W2155141930 @default.
- W2803781749 cites W2164330572 @default.
- W2803781749 cites W2191253925 @default.
- W2803781749 cites W2338318698 @default.
- W2803781749 cites W2411489059 @default.
- W2803781749 cites W2525713298 @default.
- W2803781749 cites W2562319768 @default.
- W2803781749 doi "https://doi.org/10.1109/access.2018.2839340" @default.
- W2803781749 hasPublicationYear "2018" @default.
- W2803781749 type Work @default.
- W2803781749 sameAs 2803781749 @default.
- W2803781749 citedByCount "18" @default.
- W2803781749 countsByYear W28037817492018 @default.
- W2803781749 countsByYear W28037817492019 @default.
- W2803781749 countsByYear W28037817492020 @default.
- W2803781749 countsByYear W28037817492021 @default.
- W2803781749 countsByYear W28037817492022 @default.
- W2803781749 countsByYear W28037817492023 @default.
- W2803781749 crossrefType "journal-article" @default.
- W2803781749 hasAuthorship W2803781749A5012904505 @default.
- W2803781749 hasAuthorship W2803781749A5014505938 @default.
- W2803781749 hasAuthorship W2803781749A5024632184 @default.
- W2803781749 hasAuthorship W2803781749A5030350248 @default.
- W2803781749 hasAuthorship W2803781749A5034411104 @default.
- W2803781749 hasAuthorship W2803781749A5062619125 @default.
- W2803781749 hasBestOaLocation W28037817491 @default.
- W2803781749 hasConcept C105795698 @default.
- W2803781749 hasConcept C114614502 @default.
- W2803781749 hasConcept C119857082 @default.
- W2803781749 hasConcept C154945302 @default.
- W2803781749 hasConcept C2777212361 @default.
- W2803781749 hasConcept C2780150128 @default.
- W2803781749 hasConcept C2780595030 @default.
- W2803781749 hasConcept C2780598303 @default.
- W2803781749 hasConcept C33923547 @default.
- W2803781749 hasConcept C41008148 @default.
- W2803781749 hasConcept C48372109 @default.
- W2803781749 hasConcept C50644808 @default.
- W2803781749 hasConcept C94375191 @default.
- W2803781749 hasConceptScore W2803781749C105795698 @default.
- W2803781749 hasConceptScore W2803781749C114614502 @default.
- W2803781749 hasConceptScore W2803781749C119857082 @default.
- W2803781749 hasConceptScore W2803781749C154945302 @default.
- W2803781749 hasConceptScore W2803781749C2777212361 @default.
- W2803781749 hasConceptScore W2803781749C2780150128 @default.
- W2803781749 hasConceptScore W2803781749C2780595030 @default.