Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200122682> ?p ?o ?g. }
- W4200122682 endingPage "20" @default.
- W4200122682 startingPage "1" @default.
- W4200122682 abstract "Many real world datasets may contain missing values for various reasons. These incomplete datasets can pose severe issues to the underlying machine learning algorithms and decision support systems. It may result in high computational cost, skewed output and invalid deductions. Various solutions exist to mitigate this issue; the most popular strategy is to estimate the missing values by applying inferential techniques such as linear regression, decision trees or Bayesian inference. In this paper, the missing data problem is discussed in detail with a comprehensive review of the approaches to tackle it. The paper concludes with a discussion on the effectiveness of three imputation methods namely, imputation based on Multiple Linear Regression (MLR), Predictive Mean Matching (PMM) and Classification And Regression Tree (CART) in the context of subspace clustering. The experimental results obtained on real benchmark datasets and high-dimensional synthetic datasets highlight that, MLR based imputation method is more efficient on high-dimensional incomplete datasets." @default.
- W4200122682 created "2021-12-31" @default.
- W4200122682 creator A5091099184 @default.
- W4200122682 date "2022-04-08" @default.
- W4200122682 modified "2023-09-25" @default.
- W4200122682 title "Missing Data Imputation" @default.
- W4200122682 cites W1537066827 @default.
- W4200122682 cites W1594031697 @default.
- W4200122682 cites W1598553907 @default.
- W4200122682 cites W1841820628 @default.
- W4200122682 cites W1919216911 @default.
- W4200122682 cites W1931826507 @default.
- W4200122682 cites W1963814214 @default.
- W4200122682 cites W1964065566 @default.
- W4200122682 cites W1977561074 @default.
- W4200122682 cites W1986641583 @default.
- W4200122682 cites W2043606064 @default.
- W4200122682 cites W2060555982 @default.
- W4200122682 cites W2100358124 @default.
- W4200122682 cites W2118502261 @default.
- W4200122682 cites W2133494987 @default.
- W4200122682 cites W2147316323 @default.
- W4200122682 cites W2169076391 @default.
- W4200122682 cites W228914182 @default.
- W4200122682 cites W2509157981 @default.
- W4200122682 cites W2523799825 @default.
- W4200122682 cites W2615186871 @default.
- W4200122682 cites W2751562219 @default.
- W4200122682 cites W2754768167 @default.
- W4200122682 cites W2757870011 @default.
- W4200122682 cites W2772803673 @default.
- W4200122682 cites W2778544885 @default.
- W4200122682 cites W2897852178 @default.
- W4200122682 cites W2914685653 @default.
- W4200122682 cites W2949107635 @default.
- W4200122682 cites W2951501478 @default.
- W4200122682 cites W2978662244 @default.
- W4200122682 cites W2997739739 @default.
- W4200122682 cites W3004152197 @default.
- W4200122682 cites W3005886042 @default.
- W4200122682 cites W3034856749 @default.
- W4200122682 cites W3035443326 @default.
- W4200122682 cites W3043668670 @default.
- W4200122682 cites W3046271428 @default.
- W4200122682 cites W3090442497 @default.
- W4200122682 cites W3101361212 @default.
- W4200122682 cites W3110785452 @default.
- W4200122682 cites W3112244991 @default.
- W4200122682 cites W3120446419 @default.
- W4200122682 cites W3121644786 @default.
- W4200122682 cites W3124990582 @default.
- W4200122682 cites W3126095025 @default.
- W4200122682 cites W3129809292 @default.
- W4200122682 cites W3130740671 @default.
- W4200122682 cites W3149770877 @default.
- W4200122682 cites W3154315590 @default.
- W4200122682 cites W3156080831 @default.
- W4200122682 cites W3158068029 @default.
- W4200122682 cites W3168221407 @default.
- W4200122682 cites W3171020656 @default.
- W4200122682 cites W3173035319 @default.
- W4200122682 cites W3176559823 @default.
- W4200122682 cites W3177149917 @default.
- W4200122682 cites W4230687801 @default.
- W4200122682 cites W4230737036 @default.
- W4200122682 doi "https://doi.org/10.4018/ijdsst.292446" @default.
- W4200122682 hasPublicationYear "2022" @default.
- W4200122682 type Work @default.
- W4200122682 citedByCount "2" @default.
- W4200122682 countsByYear W42001226822022 @default.
- W4200122682 countsByYear W42001226822023 @default.
- W4200122682 crossrefType "journal-article" @default.
- W4200122682 hasAuthorship W4200122682A5091099184 @default.
- W4200122682 hasConcept C105795698 @default.
- W4200122682 hasConcept C119857082 @default.
- W4200122682 hasConcept C124101348 @default.
- W4200122682 hasConcept C154945302 @default.
- W4200122682 hasConcept C33923547 @default.
- W4200122682 hasConcept C41008148 @default.
- W4200122682 hasConcept C58041806 @default.
- W4200122682 hasConcept C83546350 @default.
- W4200122682 hasConcept C84525736 @default.
- W4200122682 hasConcept C9357733 @default.
- W4200122682 hasConceptScore W4200122682C105795698 @default.
- W4200122682 hasConceptScore W4200122682C119857082 @default.
- W4200122682 hasConceptScore W4200122682C124101348 @default.
- W4200122682 hasConceptScore W4200122682C154945302 @default.
- W4200122682 hasConceptScore W4200122682C33923547 @default.
- W4200122682 hasConceptScore W4200122682C41008148 @default.
- W4200122682 hasConceptScore W4200122682C58041806 @default.
- W4200122682 hasConceptScore W4200122682C83546350 @default.
- W4200122682 hasConceptScore W4200122682C84525736 @default.
- W4200122682 hasConceptScore W4200122682C9357733 @default.
- W4200122682 hasIssue "1" @default.
- W4200122682 hasLocation W42001226821 @default.
- W4200122682 hasOpenAccess W4200122682 @default.
- W4200122682 hasPrimaryLocation W42001226821 @default.
- W4200122682 hasRelatedWork W2032441932 @default.