Article

An iterative method for multi-class cost-sensitive learning

Authors:

Bianca Zadrozny,

John LangfordAuthors Info & Claims

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 3 - 11

https://doi.org/10.1145/1014052.1014056

Published: 22 August 2004 Publication History

Abstract

Cost-sensitive learning addresses the issue of classification in the presence of varying costs associated with different types of misclassification. In this paper, we present a method for solving multi-class cost-sensitive learning problems using any binary classification algorithm. This algorithm is derived using hree key ideas: 1) iterative weighting; 2) expanding data space; and 3) gradient boosting with stochastic ensembles. We establish some theoretical guarantees concerning the performance of this method. In particular, we show that a certain variant possesses the boosting property, given a form of weak learning assumption on the component binary classifier. We also empirically evaluate the performance of the proposed method using benchmark data sets and verify that our method generally achieves better results than representative methods for cost-sensitive learning, in terms of predictive performance (cost minimization) and, in many cases, computational efficiency.

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Cited By

Delgado RFernández-Peláez FPallarés NDiaz-Brito VIzquierdo EOriol ISimonetti ATebé CVidela SCarratalà J(2024)Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithmScientific Reports10.1038/s41598-024-77386-714:1Online publication date: 18-Nov-2024
https://doi.org/10.1038/s41598-024-77386-7
Kumar P(2024)Adversarial attacks and defenses for large language models (LLMs): methods, frameworks & challengesInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00334-813:3Online publication date: 25-Jun-2024
https://doi.org/10.1007/s13735-024-00334-8
Zhang YTang LHuang YMa Y(2024)Smart data augmentation: One equation is all you needStatistical Analysis and Data Mining: The ASA Data Science Journal10.1002/sam.1167217:2Online publication date: 27-Mar-2024
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Index Terms

An iterative method for multi-class cost-sensitive learning
1. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2004

874 pages

ISBN:1581138881

DOI:10.1145/1014052

General Chairs:
Won Kim
Cyber Database Solutions
,
Ronny Kohavi
Amazon.com
,
Program Chairs:
Johannes Gehrke
Cornell University
,
William DuMouchel
AT&T Labs Research

Copyright © 2004 ACM.

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Published: 22 August 2004

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August 22 - 25, 2004

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Delgado RFernández-Peláez FPallarés NDiaz-Brito VIzquierdo EOriol ISimonetti ATebé CVidela SCarratalà J(2024)Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithmScientific Reports10.1038/s41598-024-77386-714:1Online publication date: 18-Nov-2024
https://doi.org/10.1038/s41598-024-77386-7
Kumar P(2024)Adversarial attacks and defenses for large language models (LLMs): methods, frameworks & challengesInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00334-813:3Online publication date: 25-Jun-2024
https://doi.org/10.1007/s13735-024-00334-8
Zhang YTang LHuang YMa Y(2024)Smart data augmentation: One equation is all you needStatistical Analysis and Data Mining: The ASA Data Science Journal10.1002/sam.1167217:2Online publication date: 27-Mar-2024
https://doi.org/10.1002/sam.11672
Li ZChen JLaber ELiu FBaumgartner R(2023)Optimal Treatment Regimes: A Review and Empirical ComparisonInternational Statistical Review10.1111/insr.1253691:3(427-463)Online publication date: 22-Feb-2023
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Shen HChen SWang RWang X(2023)Adversarial Learning With Cost-Sensitive ClassesIEEE Transactions on Cybernetics10.1109/TCYB.2022.314638853:8(4855-4866)Online publication date: Aug-2023
https://doi.org/10.1109/TCYB.2022.3146388
Yadav AShekhar SVidyarthi APrakash RGowri R(2023)Hyper-Parameter Tuning with Grid and Randomized Search Techniques for Predictive Models of Hotel Booking2023 International Conference on Electrical, Electronics, Communication and Computers (ELEXCOM)10.1109/ELEXCOM58812.2023.10370718(1-5)Online publication date: 26-Aug-2023
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Kumaravel AVijayan T(2023)Comparing cost sensitive classifiers by the false-positive to false- negative ratio in diagnostic studiesExpert Systems with Applications10.1016/j.eswa.2023.120303227(120303)Online publication date: Oct-2023
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Duan YLiu XJatowt AYu HLynden SKim KMatono A(2022)Long-Tailed Graph Representation Learning via Dual Cost-Sensitive Graph Convolutional NetworkRemote Sensing10.3390/rs1414329514:14(3295)Online publication date: 8-Jul-2022
https://doi.org/10.3390/rs14143295
Duan YLiu XJatowt AYu HLynden SKim KMatono A(2022)Dual Cost-sensitive Graph Convolutional Network2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892598(1-8)Online publication date: 18-Jul-2022
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Garg ASani DAnand S(2022)Learning Hierarchy Aware Features for Reducing Mistake SeverityComputer Vision – ECCV 202210.1007/978-3-031-20053-3_15(252-267)Online publication date: 6-Nov-2022
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