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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2013: Advances in Knowledge Discovery and Data Mining pp 317–328Cite as

ProWSyn: Proximity Weighted Synthetic Oversampling Technique for Imbalanced Data Set Learning

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7819)

Abstract

An imbalanced data set creates severe problems for the classifier as number of samples of one class (majority) is much higher than the other class (minority). Synthetic oversampling methods address this problem by generating new synthetic minority class samples. To distribute the synthetic samples effectively, recent approaches create weight values for original minority samples based on their importance and distribute synthetic samples according to weight values. However, most of the existing algorithms create inappropriate weights and in many cases, they cannot generate the required weight values for the minority samples. This results in a poor distribution of generated synthetic samples. In this respect, this paper presents a new synthetic oversampling algorithm, Proximity Weighted Synthetic Oversampling Technique (ProWSyn). Our proposed algorithm generate effective weight values for the minority data samples based on sample’s proximity information, i.e., distance from boundary which results in a proper distribution of generated synthetic samples across the minority data set. Simulation results on some real world datasets shows the effectiveness of the proposed method showing improvements in various assessment metrics such as AUC, F-measure, and G-mean.

Keywords

  • Imbalanced learning
  • clustering
  • synthetic oversampling

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  • DOI: 10.1007/978-3-642-37456-2_27
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Author information

Authors and Affiliations

  1. Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh

    Sukarna Barua & Md. Monirul Islam

  2. University of Fukui, Fukui, Japan

    Kazuyuki Murase

Authors
  1. Sukarna Barua
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  2. Md. Monirul Islam
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  3. Kazuyuki Murase
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Editor information

Editors and Affiliations

  1. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  2. Dept. of Computer Science and Information Engineering, Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, P.O. Box 123, 2007, Sydney, NSW, Australia

    Longbing Cao & Guandong Xu & 

  4. Asian Office of Aerospace Research and Development (AOARD), Air Force Office of Scientific Research (AFOSR), Air Force Research Laboratory USA, Osaka University, 7-23-17 Roppongi, 106-0032, Minato-ku, Tokyo, Japan

    Hiroshi Motoda

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Barua, S., Islam, M.M., Murase, K. (2013). ProWSyn: Proximity Weighted Synthetic Oversampling Technique for Imbalanced Data Set Learning. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_27

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  • DOI: https://doi.org/10.1007/978-3-642-37456-2_27

  • Publisher Name: Springer, Berlin, Heidelberg

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