research-article

Asymmetric support vector machines: low false-positive learning under the user tolerance

Authors:

Chung-Min Chen,

Ming-Syan ChenAuthors Info & Claims

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 749 - 757

https://doi.org/10.1145/1401890.1401980

Published: 24 August 2008 Publication History

Abstract

Many practical applications of classification require the classifier to produce a very low false-positive rate. Although the Support Vector Machine (SVM) has been widely applied to these applications due to its superiority in handling high dimensional data, there are relatively little effort other than setting a threshold or changing the costs of slacks to ensure the low false-positive rate. In this paper, we propose the notion of Asymmetric Support VectorMachine (ASVM) that takes into account the false-positives and the user tolerance in its objective. Such a new objective formulation allows us to raise the confidence in predicting the positives, and therefore obtain a lower chance of false-positives. We study the effects of the parameters in ASVM objective and address some implementation issues related to the Sequential Minimal Optimization (SMO) to cope with large-scale data. An extensive simulation is conducted and shows that ASVM is able to yield either noticeable improvement in performance or reduction in training time as compared to the previous arts.

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Index Terms

Asymmetric support vector machines: low false-positive learning under the user tolerance
1. Computing methodologies
  1. Machine learning
    1. Learning settings
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2008

1116 pages

ISBN:9781605581934

DOI:10.1145/1401890

General Chair:
Ying Li
Microsoft adCenter Labs
,
Program Chairs:
Bing Liu
University of Illinois at Chicago
,
Sunita Sarawagi
Indian Institute of Technology, Bombay

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 August 2008

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August 24 - 27, 2008

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KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;

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

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https://doi.org/10.1145/3543507.3583223
Liu JLei JLiao ZHe J(2023)Software defect prediction model based on improved twin support vector machinesSoft Computing10.1007/s00500-023-07984-627:21(16101-16110)Online publication date: 1-Apr-2023
https://doi.org/10.1007/s00500-023-07984-6
Yang TYing Y(2022)AUC Maximization in the Era of Big Data and AI: A SurveyACM Computing Surveys10.1145/355472955:8(1-37)Online publication date: 3-Aug-2022
https://dl.acm.org/doi/10.1145/3554729
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