Matches in SemOpenAlex for { <https://semopenalex.org/work/W2327706228> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W2327706228 endingPage "249" @default.
- W2327706228 startingPage "243" @default.
- W2327706228 abstract "Credit risk is the most challenging risk to which financial institution are exposed. Credit scoring is the main analytical technique for credit risk evaluation. Application of artificial intelligence has lead to better performance of credit scoring models. In this paper a hybrid model for credit scoring is designed which applies ensemble learning for credit granting decisions. Ten classifier agents are utilized as the members of ensemble model. Support vector machine, Neural Networks and Decision Tree as base classifiers were compared based on their accuracy in classification. Since even a small improvement in credit scoring accuracy causes significant loss reduction, then the utilization of best classification model is of a great importance. A real dataset was used to test the model and classifiers. The test results showed that proposed hybrid ensemble model has better classification accuracy and performance when compared to other credit scoring methods. In addition, among three classifiers, the support Vector Machine had the best performance and accuracy." @default.
- W2327706228 created "2016-06-24" @default.
- W2327706228 creator A5001382895 @default.
- W2327706228 creator A5013346063 @default.
- W2327706228 date "2011-01-01" @default.
- W2327706228 modified "2023-09-26" @default.
- W2327706228 title "Application of Artificial Intelligence Techniques for Credit Risk Evaluation" @default.
- W2327706228 cites W1496929357 @default.
- W2327706228 cites W1521843029 @default.
- W2327706228 cites W1968023063 @default.
- W2327706228 cites W1971321416 @default.
- W2327706228 cites W1981093066 @default.
- W2327706228 cites W1987970207 @default.
- W2327706228 cites W1988474781 @default.
- W2327706228 cites W1990368529 @default.
- W2327706228 cites W1997740464 @default.
- W2327706228 cites W2004076523 @default.
- W2327706228 cites W2011108441 @default.
- W2327706228 cites W2015768982 @default.
- W2327706228 cites W2035491076 @default.
- W2327706228 cites W2035908274 @default.
- W2327706228 cites W2036547589 @default.
- W2327706228 cites W2059693241 @default.
- W2327706228 cites W2062634819 @default.
- W2327706228 cites W2089811952 @default.
- W2327706228 cites W2089823888 @default.
- W2327706228 cites W2093829413 @default.
- W2327706228 cites W2103780778 @default.
- W2327706228 cites W2105210673 @default.
- W2327706228 cites W2113076747 @default.
- W2327706228 cites W2115682519 @default.
- W2327706228 cites W2117750904 @default.
- W2327706228 cites W2121069620 @default.
- W2327706228 cites W2124532504 @default.
- W2327706228 cites W2147695462 @default.
- W2327706228 cites W2148160366 @default.
- W2327706228 cites W2156799400 @default.
- W2327706228 cites W2159468771 @default.
- W2327706228 cites W2165063012 @default.
- W2327706228 cites W3122651343 @default.
- W2327706228 doi "https://doi.org/10.7763/ijmo.2011.v1.43" @default.
- W2327706228 hasPublicationYear "2011" @default.
- W2327706228 type Work @default.
- W2327706228 sameAs 2327706228 @default.
- W2327706228 citedByCount "24" @default.
- W2327706228 countsByYear W23277062282013 @default.
- W2327706228 countsByYear W23277062282014 @default.
- W2327706228 countsByYear W23277062282017 @default.
- W2327706228 countsByYear W23277062282018 @default.
- W2327706228 countsByYear W23277062282019 @default.
- W2327706228 countsByYear W23277062282020 @default.
- W2327706228 countsByYear W23277062282021 @default.
- W2327706228 countsByYear W23277062282022 @default.
- W2327706228 countsByYear W23277062282023 @default.
- W2327706228 crossrefType "journal-article" @default.
- W2327706228 hasAuthorship W2327706228A5001382895 @default.
- W2327706228 hasAuthorship W2327706228A5013346063 @default.
- W2327706228 hasConcept C154945302 @default.
- W2327706228 hasConcept C41008148 @default.
- W2327706228 hasConceptScore W2327706228C154945302 @default.
- W2327706228 hasConceptScore W2327706228C41008148 @default.
- W2327706228 hasLocation W23277062281 @default.
- W2327706228 hasOpenAccess W2327706228 @default.
- W2327706228 hasPrimaryLocation W23277062281 @default.
- W2327706228 hasRelatedWork W1596801655 @default.
- W2327706228 hasRelatedWork W2130043461 @default.
- W2327706228 hasRelatedWork W2350741829 @default.
- W2327706228 hasRelatedWork W2358668433 @default.
- W2327706228 hasRelatedWork W2376932109 @default.
- W2327706228 hasRelatedWork W2382290278 @default.
- W2327706228 hasRelatedWork W2390279801 @default.
- W2327706228 hasRelatedWork W2748952813 @default.
- W2327706228 hasRelatedWork W2899084033 @default.
- W2327706228 hasRelatedWork W2530322880 @default.
- W2327706228 isParatext "false" @default.
- W2327706228 isRetracted "false" @default.
- W2327706228 magId "2327706228" @default.
- W2327706228 workType "article" @default.