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

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An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data. / Ozan, Ezgi Can; Riabchenko, Ekaterina; Kiranyaz, Serkan; Gabbouj, Moncef.

Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings. Springer, 2016. p. 387-392 (Lecture Notes in Computer Science; Vol. 9897).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Ozan, EC, Riabchenko, E, Kiranyaz, S & Gabbouj, M 2016, An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data. in Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings. Lecture Notes in Computer Science, vol. 9897, Springer, pp. 387-392, INTERNATIONAL SYMPOSIUM ON INTELLIGENT DATA ANALYSIS, 1/01/00. https://doi.org/10.1007/978-3-319-46349-0_34

APA

Ozan, E. C., Riabchenko, E., Kiranyaz, S., & Gabbouj, M. (2016). An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data. In Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings (pp. 387-392). (Lecture Notes in Computer Science; Vol. 9897). Springer. https://doi.org/10.1007/978-3-319-46349-0_34

Vancouver

Ozan EC, Riabchenko E, Kiranyaz S, Gabbouj M. An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data. In Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings. Springer. 2016. p. 387-392. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-46349-0_34

Author

Ozan, Ezgi Can ; Riabchenko, Ekaterina ; Kiranyaz, Serkan ; Gabbouj, Moncef. / An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data. Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings. Springer, 2016. pp. 387-392 (Lecture Notes in Computer Science).

Bibtex - Download

@inproceedings{eaa991555e554c4fa0c3aeb2d10032bf,
title = "An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data",
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.",
author = "Ozan, {Ezgi Can} and Ekaterina Riabchenko and Serkan Kiranyaz and Moncef Gabbouj",
note = "EXT={"}Kiranyaz, Serkan{"}",
year = "2016",
doi = "10.1007/978-3-319-46349-0_34",
language = "English",
isbn = "978-3-319-46348-3",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "387--392",
booktitle = "Advances in Intelligent Data Analysis XV",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data

AU - Ozan, Ezgi Can

AU - Riabchenko, Ekaterina

AU - Kiranyaz, Serkan

AU - Gabbouj, Moncef

N1 - EXT="Kiranyaz, Serkan"

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-46349-0_34

DO - 10.1007/978-3-319-46349-0_34

M3 - Conference contribution

SN - 978-3-319-46348-3

T3 - Lecture Notes in Computer Science

SP - 387

EP - 392

BT - Advances in Intelligent Data Analysis XV

PB - Springer

ER -