Abstract

Random forest is a well-known and widely-used machine learning model. In many applications where the training data arise from real-world sources, there may be labeling errors in the data. In spite of its superior performance, the basic model of random forest dose not consider potential label noise in learning, and thus its performance can suffer significantly in the presence of label noise. In order to solve this problem, we present a new variation of random forest - a novel learning approach that leads to an improved noise robust random forest (NRRF) model. We incorporate the noise information by introducing a global multi-class noise tolerant loss function into the training of the classic random forest model. This new loss function was found to significantly boost the performance of random forest. We evaluated the proposed NRRF by extensive experiments of classification tasks on standard machine learning/computer vision datasets like MNIST, letter and Cifar10. The proposed NRRF produced very promising results under a wide range of noise settings.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages950-958
Number of pages9
ISBN (Electronic)9781728119755
DOIs
StatePublished - Mar 4 2019
Event19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019 - Waikoloa Village, United States
Duration: Jan 7 2019Jan 11 2019

Publication series

NameProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019

Conference

Conference19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019
CountryUnited States
CityWaikoloa Village
Period1/7/191/11/19

Fingerprint

Labels
Learning systems
Labeling
Computer vision
Experiments

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Zhou, X., Ding, P. L. K., & Li, B. (2019). Improving robustness of random forest under label noise. In Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019 (pp. 950-958). [8658395] (Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV.2019.00106

Improving robustness of random forest under label noise. / Zhou, Xu; Ding, Pak Lun Kevin; Li, Baoxin.

Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 950-958 8658395 (Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhou, X, Ding, PLK & Li, B 2019, Improving robustness of random forest under label noise. in Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019., 8658395, Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Institute of Electrical and Electronics Engineers Inc., pp. 950-958, 19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Waikoloa Village, United States, 1/7/19. https://doi.org/10.1109/WACV.2019.00106
Zhou X, Ding PLK, Li B. Improving robustness of random forest under label noise. In Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 950-958. 8658395. (Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019). https://doi.org/10.1109/WACV.2019.00106
Zhou, Xu ; Ding, Pak Lun Kevin ; Li, Baoxin. / Improving robustness of random forest under label noise. Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 950-958 (Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019).
@inproceedings{dd6d9d10fd844deda3f306c93d1c79d0,
title = "Improving robustness of random forest under label noise",
abstract = "Random forest is a well-known and widely-used machine learning model. In many applications where the training data arise from real-world sources, there may be labeling errors in the data. In spite of its superior performance, the basic model of random forest dose not consider potential label noise in learning, and thus its performance can suffer significantly in the presence of label noise. In order to solve this problem, we present a new variation of random forest - a novel learning approach that leads to an improved noise robust random forest (NRRF) model. We incorporate the noise information by introducing a global multi-class noise tolerant loss function into the training of the classic random forest model. This new loss function was found to significantly boost the performance of random forest. We evaluated the proposed NRRF by extensive experiments of classification tasks on standard machine learning/computer vision datasets like MNIST, letter and Cifar10. The proposed NRRF produced very promising results under a wide range of noise settings.",
author = "Xu Zhou and Ding, {Pak Lun Kevin} and Baoxin Li",
year = "2019",
month = "3",
day = "4",
doi = "10.1109/WACV.2019.00106",
language = "English (US)",
series = "Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "950--958",
booktitle = "Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019",

}

TY - GEN

T1 - Improving robustness of random forest under label noise

AU - Zhou, Xu

AU - Ding, Pak Lun Kevin

AU - Li, Baoxin

PY - 2019/3/4

Y1 - 2019/3/4

N2 - Random forest is a well-known and widely-used machine learning model. In many applications where the training data arise from real-world sources, there may be labeling errors in the data. In spite of its superior performance, the basic model of random forest dose not consider potential label noise in learning, and thus its performance can suffer significantly in the presence of label noise. In order to solve this problem, we present a new variation of random forest - a novel learning approach that leads to an improved noise robust random forest (NRRF) model. We incorporate the noise information by introducing a global multi-class noise tolerant loss function into the training of the classic random forest model. This new loss function was found to significantly boost the performance of random forest. We evaluated the proposed NRRF by extensive experiments of classification tasks on standard machine learning/computer vision datasets like MNIST, letter and Cifar10. The proposed NRRF produced very promising results under a wide range of noise settings.

AB - Random forest is a well-known and widely-used machine learning model. In many applications where the training data arise from real-world sources, there may be labeling errors in the data. In spite of its superior performance, the basic model of random forest dose not consider potential label noise in learning, and thus its performance can suffer significantly in the presence of label noise. In order to solve this problem, we present a new variation of random forest - a novel learning approach that leads to an improved noise robust random forest (NRRF) model. We incorporate the noise information by introducing a global multi-class noise tolerant loss function into the training of the classic random forest model. This new loss function was found to significantly boost the performance of random forest. We evaluated the proposed NRRF by extensive experiments of classification tasks on standard machine learning/computer vision datasets like MNIST, letter and Cifar10. The proposed NRRF produced very promising results under a wide range of noise settings.

UR - http://www.scopus.com/inward/record.url?scp=85063576976&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063576976&partnerID=8YFLogxK

U2 - 10.1109/WACV.2019.00106

DO - 10.1109/WACV.2019.00106

M3 - Conference contribution

T3 - Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019

SP - 950

EP - 958

BT - Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019

PB - Institute of Electrical and Electronics Engineers Inc.

ER -