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A semantic framework for noise addition with nominal data

  • Identification data

    Identifier: imarina:5130932
    Authors:
    Rodriguez-Garcia, MercedesBatet, MontserratSanchez, David
    Abstract:
    Noise addition is a data distortion technique widely used in data intensive applications. For example, in machine learning tasks it helps to reduce overfitting, whereas in data privacy protection it adds uncertainty to personally identifiable information. Yet, due to its mathematical operating principle, noise addition is a method mainly intended for continuous numerical data. In fact, despite the large amount of nominal data that are being currently compiled and used in data analysis, only a few alternative techniques have been proposed to distort nominal data in a similar way as standard noise addition does for numerical data. Furthermore, all these alternative methods rely on the distribution of the data rather than on the semantics of nominal values, which negatively affects the utility of the distorted outcomes. To tackle this issue, in this paper we present a semantically-grounded alternative to numerical noise suitable for nominal data, which we name semantic noise. By means of semantic noise, and by exploiting structured knowledge sources such as ontologies, we are able to distort nominal data while preserving better their semantics and thus, their analytical utility. To that end, we provide semantically and mathematically coherent versions of the statistical operators required in the noise addition process, which include the difference, the mean, the variance and the covariance. Then, we propose semantic noise addition algorithms that cope with the finite, discrete and non-ordinal nature of nominal data. The proposed algorithms cover both uncorrelated noise addition, which is suited to independent attributes, and correlated noise addition, which can cope with multivariate datasets with dependent attributes. Empirical results show that our proposals offer genera
  • Others:

    Author, as appears in the article.: Rodriguez-Garcia, Mercedes; Batet, Montserrat; Sanchez, David
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Batet Sanromà, Montserrat / Sánchez Ruenes, David
    Keywords: Semantics Ontologies Nominal data Noise addition Medical ontologies
    Abstract: Noise addition is a data distortion technique widely used in data intensive applications. For example, in machine learning tasks it helps to reduce overfitting, whereas in data privacy protection it adds uncertainty to personally identifiable information. Yet, due to its mathematical operating principle, noise addition is a method mainly intended for continuous numerical data. In fact, despite the large amount of nominal data that are being currently compiled and used in data analysis, only a few alternative techniques have been proposed to distort nominal data in a similar way as standard noise addition does for numerical data. Furthermore, all these alternative methods rely on the distribution of the data rather than on the semantics of nominal values, which negatively affects the utility of the distorted outcomes. To tackle this issue, in this paper we present a semantically-grounded alternative to numerical noise suitable for nominal data, which we name semantic noise. By means of semantic noise, and by exploiting structured knowledge sources such as ontologies, we are able to distort nominal data while preserving better their semantics and thus, their analytical utility. To that end, we provide semantically and mathematically coherent versions of the statistical operators required in the noise addition process, which include the difference, the mean, the variance and the covariance. Then, we propose semantic noise addition algorithms that cope with the finite, discrete and non-ordinal nature of nominal data. The proposed algorithms cover both uncorrelated noise addition, which is suited to independent attributes, and correlated noise addition, which can cope with multivariate datasets with dependent attributes. Empirical results show that our proposals offer general and configurable mechanisms to distort nominal data while preserving data semantics better than baseline methods based only on the distribution of the data.
    Thematic Areas: Software Matemática / probabilidade e estatística Management information systems Interdisciplinar Information systems and management Información y documentación Engenharias iv Engenharias iii Economia Computer science, artificial intelligence Ciencias sociales Ciências biológicas i Ciência da computação Astronomia / física Artificial intelligence Administração pública e de empresas, ciências contábeis e turismo
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: montserrat.batet@urv.cat david.sanchez@urv.cat
    Author identifier: 0000-0001-8174-7592 0000-0001-7275-7887
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S0950705117300473
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Knowledge-Based Systems. 122 103-118
    APA: Rodriguez-Garcia, Mercedes; Batet, Montserrat; Sanchez, David (2017). A semantic framework for noise addition with nominal data. Knowledge-Based Systems, 122(), 103-118. DOI: 10.1016/j.knosys.2017.01.032
    Article's DOI: 10.1016/j.knosys.2017.01.032
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2017
    Publication Type: Journal Publications
  • Keywords:

    Artificial Intelligence,Computer Science, Artificial Intelligence,Information Systems and Management,Management Information Systems,Software
    Semantics
    Ontologies
    Nominal data
    Noise addition
    Medical ontologies
    Software
    Matemática / probabilidade e estatística
    Management information systems
    Interdisciplinar
    Information systems and management
    Información y documentación
    Engenharias iv
    Engenharias iii
    Economia
    Computer science, artificial intelligence
    Ciencias sociales
    Ciências biológicas i
    Ciência da computação
    Astronomia / física
    Artificial intelligence
    Administração pública e de empresas, ciências contábeis e turismo
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