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AuthorOzan, Ezgi Can
AuthorRiabchenko, Ekaterina
AuthorKiranyaz, Serkan
AuthorGabbouj, Moncef
Available date2021-09-05T05:40:12Z
Publication Date2016
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
ISSN3029743
URIhttp://dx.doi.org/10.1007/978-3-319-46349-0_34
URIhttp://hdl.handle.net/10576/22674
AbstractIn this paper, we describe our solution for the machine learning prediction challenge in IDA 2016. For the given problem of 2-class classification on an imbalanced dataset with missing data, we first develop an imputation method based on k-NN to estimate the missing values. Then we define a tailored representation for the given problem as an optimization scheme, which consists of learned distance and voting weights for k-NN classification. The proposed solution performs better in terms of the given challenge metric compared to the traditional classification methods such as SVM, AdaBoost or Random Forests. Springer International Publishing AG 2016.
Languageen
PublisherSpringer Verlag
SubjectImbalanced datasets
K-NN classifier
Missing data
TitleAn optimized k-NN approach for classification on imbalanced datasets with missing data
TypeConference Paper
Pagination387-392
Volume Number9897 LNCS
dc.accessType Abstract Only


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