A new heuristic approach for treating missing value: ABCIMP

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dc.authorscopusid56539994200
dc.authorscopusid55808009200
dc.contributor.authorCihan, Pınar
dc.contributor.authorÖzger, Z.B.
dc.date.accessioned2022-05-11T14:15:54Z
dc.date.available2022-05-11T14:15:54Z
dc.date.issued2019
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractMissing values in datasets present an important problem for traditional and modern statistical methods. Many statistical methods have been developed to analyze the complete datasets. However, most of the real world datasets contain missing values. Therefore, in recent years, many methods have been developed to overcome the missing value problem. Heuristic methods have become popular in this field due to their superior performance in many other optimization problems. This paper introduces an Artificial Bee Colony algorithm based new approach for missing value imputation in the four real-world discrete datasets. At the proposed Artificial Bee Colony Imputation (ABCimp) method, Bayesian Optimization is integrated into the Artificial Bee Colony algorithm. The performance of the proposed technique is compared with other well-known six methods, which are Mean, Median, k Nearest Neighbor (k-NN), Multivariate Equation by Chained Equation (MICE), Singular Value Decomposition (SVD), and MissForest (MF). The classification error and root mean square error are used as the evaluation criteria of the imputation methods performance and the Naive Bayes algorithm is used as the classifier. The empirical results show that state-of-the-art ABCimp performs better than the other most popular imputation methods at the variable missing rates ranging from 3 % to 15 %. © 2019 Kauno Technologijos Universitetas. All rights reserved.
dc.identifier.doi10.5755/j01.eie.25.6.24826
dc.identifier.endpage54
dc.identifier.issn1392-1215
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85077474905
dc.identifier.scopusqualityQ3
dc.identifier.startpage48
dc.identifier.urihttps://doi.org/10.5755/j01.eie.25.6.24826
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6116
dc.identifier.volume25
dc.indekslendigikaynakScopus
dc.institutionauthorCihan, Pınar
dc.language.isoen
dc.publisherKauno Technologijos Universitetas
dc.relation.ispartofElektronika ir Elektrotechnika
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBayes methods
dc.subjectData handling
dc.subjectEvolutionary computation
dc.subjectHeuristic algorithms
dc.titleA new heuristic approach for treating missing value: ABCIMP
dc.typeArticle

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