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Refined Weighted Random Forest and Its Application to Credit Card Fraud Detection

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Computational Data and Social Networks (CSoNet 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11280))

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Abstract

Random forest (RF) is widely used in many applications due to good classification performance. However, its voting mechanism assumes that all base classifiers have the same weight. In fact, it is more reasonable that some have relatively high weights while some have relatively low weights because the randomization of bootstrap sampling and attributes selecting cannot guarantee all trees have the same ability of making decision. We mainly focus on the weighted voting mechanism and then propose a novel weighted RF in this paper. Experiments on 6 public datasets illustrate that our method outperforms the RF and another weighted RF. We apply our method to credit card fraud detection and experiments also show that our method is the best.

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Acknowledgments

Authors would like to thank reviewers for their helpful comments, and also thank Professor Changjun Jiang who provides authors a lot of assistance on data and experiments. This paper is supported in part by the National Natural Science Foundation of China under grand no. 61572360 and in part by the Shanghai Shuguang Program under grant no. 15SG18. Corresponding author is G.J. Liu.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China

    Shiyang Xuan, Guanjun Liu & Zhenchuan Li

Authors
  1. Shiyang Xuan
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  2. Guanjun Liu
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  3. Zhenchuan Li
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Corresponding author

Correspondence to Guanjun Liu .

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Editors and Affiliations

  1. Texas Southern University, Houston, TX, USA

    Xuemin Chen

  2. Ira A. Fulton School of Engineering, Tempe, AZ, USA

    Arunabha Sen

  3. Texas Southern University, Houston, TX, USA

    Wei Wayne Li

  4. University of Florida, Gainesville, AL, USA

    My T. Thai

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Xuan, S., Liu, G., Li, Z. (2018). Refined Weighted Random Forest and Its Application to Credit Card Fraud Detection. In: Chen, X., Sen, A., Li, W., Thai, M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science(), vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_29

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  • DOI: https://doi.org/10.1007/978-3-030-04648-4_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04647-7

  • Online ISBN: 978-3-030-04648-4

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