An optimized k-NN approach for classification on imbalanced datasets with missing data
Author | Ozan, Ezgi Can |
Author | Riabchenko, Ekaterina |
Author | Kiranyaz, Serkan |
Author | Gabbouj, Moncef |
Available date | 2021-09-05T05:40:12Z |
Publication Date | 2016 |
Publication Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Resource | Scopus |
ISSN | 3029743 |
Abstract | In 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. |
Language | en |
Publisher | Springer Verlag |
Subject | Imbalanced datasets K-NN classifier Missing data |
Type | Conference Paper |
Pagination | 387-392 |
Volume Number | 9897 LNCS |
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