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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
García-Laencina, P.J., Sancho-Gómez, J.-L., Figueiras-Vidal, A.R.: Pattern classification with missing data: a review. Neural Comput. Appl. 19(2), 263–282 (2009)
Batista, G., Monard, M.C.: A study of k-nearest neighbour as an imputation method. Hybrid Intell. Syst. 87(48), 251–260 (2002)
Wu, X., Kumar, V., Ross, Q.J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)
Dudani, S.A.: The distance-weighted k-nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC-6(4), 325–327 (1976)
Pedregosa, F., Grisel, O., Weiss, R., Passos, A., Brucher, M.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(1), 2825–2830 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Ozan, E.C., Riabchenko, E., Kiranyaz, S., Gabbouj, M. (2016). An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-46349-0_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46348-3
Online ISBN: 978-3-319-46349-0
eBook Packages: Computer ScienceComputer Science (R0)