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An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data

  • Ezgi Can OzanEmail author
  • Ekaterina Riabchenko
  • Serkan Kiranyaz
  • Moncef Gabbouj
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)

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.

Keywords

k-NN classifier Missing data Imbalanced datasets 

References

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ezgi Can Ozan
    • 1
    Email author
  • Ekaterina Riabchenko
    • 1
  • Serkan Kiranyaz
    • 2
  • Moncef Gabbouj
    • 1
  1. 1.Tampere University of TechnologyTampereFinland
  2. 2.Electrical Engineering Department, College of EngineeringQatar UniversityDohaQatar

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