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Optimal restoration of stochastic monotonicity with respect to cumulative label frequency loss functions

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Title: Optimal restoration of stochastic monotonicity with respect to cumulative label frequency loss functions
Authors: Rademaker, M.1 michael.rademaker@ugent.be, De Baets, B. bernard.debaets@ugent.be
Source: Information Sciences. Feb2011, Vol. 181 Issue 4, p747-757. 11p.
Subjects: Stochastic analysis, Data mining, Monotone operators, Multiple criteria decision making, Numerical functions, Time-frequency analysis, Frequency spectra, Comparative studies, Information science
Abstract: Abstract: A method to restore stochastic monotonicity of noisy multi-criteria data sets through relabeling is presented. By formulating the problem as a weighted maximum independent set problem on a comparability graph, it is possible to compute optimal relabelings w.r.t. cumulative label frequency loss function. We demonstrate how to formulate the problem in this manner and discuss why it requires objects to be relabeled instead of deleted. More precisely, we will formulate the zero-one cumulative label frequency loss, L1 cumulative label frequency loss and squared cumulative label frequency loss, and provide a weighing function for each. We investigate these loss functions in the related context of restoring regular monotonicity, dealing with objects with a single label, rather than distributions. Finally, we provide applications on some closely related example data sets and discuss some interesting findings. [ABSTRACT FROM AUTHOR]
Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 55642470
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  Data: Abstract: A method to restore stochastic monotonicity of noisy multi-criteria data sets through relabeling is presented. By formulating the problem as a weighted maximum independent set problem on a comparability graph, it is possible to compute optimal relabelings w.r.t. cumulative label frequency loss function. We demonstrate how to formulate the problem in this manner and discuss why it requires objects to be relabeled instead of deleted. More precisely, we will formulate the zero-one cumulative label frequency loss, L1 cumulative label frequency loss and squared cumulative label frequency loss, and provide a weighing function for each. We investigate these loss functions in the related context of restoring regular monotonicity, dealing with objects with a single label, rather than distributions. Finally, we provide applications on some closely related example data sets and discuss some interesting findings. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.ins.2010.10.026
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        Text: English
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        Type: general
      – SubjectFull: Data mining
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      – SubjectFull: Monotone operators
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      – SubjectFull: Multiple criteria decision making
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      – SubjectFull: Numerical functions
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      – SubjectFull: Time-frequency analysis
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      – SubjectFull: Frequency spectra
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      – SubjectFull: Information science
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              Text: Feb2011
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