The Experts below are selected from a list of 15 Experts worldwide ranked by ideXlab platform

T. Courregelongue - One of the best experts on this subject based on the ideXlab platform.

  • Preliminary numerical investigations of conformal predictors based on fuzzy logic classifiers
    Annals of Mathematics and Artificial Intelligence, 2015
    Co-Authors: Andrea Murari, Jesús Vega, D. Mazon, T. Courregelongue
    Abstract:

    A new family of techniques, called conformal predictors, have very recently been developed to hedge the estimates of machine learning methods, by providing two parameters, credibility and confidence, which can assess the level of trust that can be attributed to their outputs. In this paper, the main steps required to extend this approach to fuzzy logic classifiers are reported. The more delicate aspect is the definition of an appropriate Nonconformity Score, which has to be based on the fuzzy membership function to preserve the specificities of Fuzzy Logic. Various examples of increasing complexity are introduced, to describe the main properties of fuzzy logic based conformal predictors and to compare their performance with alternative approaches. The obtained results are quite promising, since conformal predictors based on fuzzy classifiers outperform solutions based on the nearest neighbour in terms of ambiguity, robustness and interpretability.

  • AIAI (2) - Introduction to Conformal Predictors Based on Fuzzy Logic Classifiers
    IFIP Advances in Information and Communication Technology, 2012
    Co-Authors: Andrea Murari, Jesús Vega, D. Mazon, T. Courregelongue
    Abstract:

    In this paper, an introduction to the main steps required to develop conformal predictors based on fuzzy logic classifiers is provided. The more delicate aspect is the definition of an appropriate Nonconformity Score, which has to be based on the membership function to preserve the specificities of Fuzzy Logic. Various examples are introduced, to describe the main properties of fuzzy logic based conformal predictors and to compare their performance with alternative approaches. The obtained results are quite promising, since conformal predictors based on fuzzy classifiers show the potential to outperform solutions based on the nearest neighbour in terms of ambiguity, robustness and interpretability

Andrea Murari - One of the best experts on this subject based on the ideXlab platform.

  • Preliminary numerical investigations of conformal predictors based on fuzzy logic classifiers
    Annals of Mathematics and Artificial Intelligence, 2015
    Co-Authors: Andrea Murari, Jesús Vega, D. Mazon, T. Courregelongue
    Abstract:

    A new family of techniques, called conformal predictors, have very recently been developed to hedge the estimates of machine learning methods, by providing two parameters, credibility and confidence, which can assess the level of trust that can be attributed to their outputs. In this paper, the main steps required to extend this approach to fuzzy logic classifiers are reported. The more delicate aspect is the definition of an appropriate Nonconformity Score, which has to be based on the fuzzy membership function to preserve the specificities of Fuzzy Logic. Various examples of increasing complexity are introduced, to describe the main properties of fuzzy logic based conformal predictors and to compare their performance with alternative approaches. The obtained results are quite promising, since conformal predictors based on fuzzy classifiers outperform solutions based on the nearest neighbour in terms of ambiguity, robustness and interpretability.

  • AIAI (2) - Introduction to Conformal Predictors Based on Fuzzy Logic Classifiers
    IFIP Advances in Information and Communication Technology, 2012
    Co-Authors: Andrea Murari, Jesús Vega, D. Mazon, T. Courregelongue
    Abstract:

    In this paper, an introduction to the main steps required to develop conformal predictors based on fuzzy logic classifiers is provided. The more delicate aspect is the definition of an appropriate Nonconformity Score, which has to be based on the membership function to preserve the specificities of Fuzzy Logic. Various examples are introduced, to describe the main properties of fuzzy logic based conformal predictors and to compare their performance with alternative approaches. The obtained results are quite promising, since conformal predictors based on fuzzy classifiers show the potential to outperform solutions based on the nearest neighbour in terms of ambiguity, robustness and interpretability

Jesús Vega - One of the best experts on this subject based on the ideXlab platform.

  • Preliminary numerical investigations of conformal predictors based on fuzzy logic classifiers
    Annals of Mathematics and Artificial Intelligence, 2015
    Co-Authors: Andrea Murari, Jesús Vega, D. Mazon, T. Courregelongue
    Abstract:

    A new family of techniques, called conformal predictors, have very recently been developed to hedge the estimates of machine learning methods, by providing two parameters, credibility and confidence, which can assess the level of trust that can be attributed to their outputs. In this paper, the main steps required to extend this approach to fuzzy logic classifiers are reported. The more delicate aspect is the definition of an appropriate Nonconformity Score, which has to be based on the fuzzy membership function to preserve the specificities of Fuzzy Logic. Various examples of increasing complexity are introduced, to describe the main properties of fuzzy logic based conformal predictors and to compare their performance with alternative approaches. The obtained results are quite promising, since conformal predictors based on fuzzy classifiers outperform solutions based on the nearest neighbour in terms of ambiguity, robustness and interpretability.

  • AIAI (2) - Introduction to Conformal Predictors Based on Fuzzy Logic Classifiers
    IFIP Advances in Information and Communication Technology, 2012
    Co-Authors: Andrea Murari, Jesús Vega, D. Mazon, T. Courregelongue
    Abstract:

    In this paper, an introduction to the main steps required to develop conformal predictors based on fuzzy logic classifiers is provided. The more delicate aspect is the definition of an appropriate Nonconformity Score, which has to be based on the membership function to preserve the specificities of Fuzzy Logic. Various examples are introduced, to describe the main properties of fuzzy logic based conformal predictors and to compare their performance with alternative approaches. The obtained results are quite promising, since conformal predictors based on fuzzy classifiers show the potential to outperform solutions based on the nearest neighbour in terms of ambiguity, robustness and interpretability

D. Mazon - One of the best experts on this subject based on the ideXlab platform.

  • Preliminary numerical investigations of conformal predictors based on fuzzy logic classifiers
    Annals of Mathematics and Artificial Intelligence, 2015
    Co-Authors: Andrea Murari, Jesús Vega, D. Mazon, T. Courregelongue
    Abstract:

    A new family of techniques, called conformal predictors, have very recently been developed to hedge the estimates of machine learning methods, by providing two parameters, credibility and confidence, which can assess the level of trust that can be attributed to their outputs. In this paper, the main steps required to extend this approach to fuzzy logic classifiers are reported. The more delicate aspect is the definition of an appropriate Nonconformity Score, which has to be based on the fuzzy membership function to preserve the specificities of Fuzzy Logic. Various examples of increasing complexity are introduced, to describe the main properties of fuzzy logic based conformal predictors and to compare their performance with alternative approaches. The obtained results are quite promising, since conformal predictors based on fuzzy classifiers outperform solutions based on the nearest neighbour in terms of ambiguity, robustness and interpretability.

  • AIAI (2) - Introduction to Conformal Predictors Based on Fuzzy Logic Classifiers
    IFIP Advances in Information and Communication Technology, 2012
    Co-Authors: Andrea Murari, Jesús Vega, D. Mazon, T. Courregelongue
    Abstract:

    In this paper, an introduction to the main steps required to develop conformal predictors based on fuzzy logic classifiers is provided. The more delicate aspect is the definition of an appropriate Nonconformity Score, which has to be based on the membership function to preserve the specificities of Fuzzy Logic. Various examples are introduced, to describe the main properties of fuzzy logic based conformal predictors and to compare their performance with alternative approaches. The obtained results are quite promising, since conformal predictors based on fuzzy classifiers show the potential to outperform solutions based on the nearest neighbour in terms of ambiguity, robustness and interpretability

Arturo J. Fernández - One of the best experts on this subject based on the ideXlab platform.

  • Most powerful lot acceptance test plans from dispersed Nonconformity counts
    The International Journal of Advanced Manufacturing Technology, 2017
    Co-Authors: Arturo J. Fernández
    Abstract:

    Optimal inspection schemes based on dispersed Nonconformity count data are derived to properly discriminate between satisfactory and unsatisfactory batches. The Conway-Maxwell-Poisson distribution is adopted to describe the stochastic behavior of the number of nonconformities per sampled unit. A mixed integer nonlinear programming problem is stated in order to determine the lot sampling plan with a minimal sample size and limited producer and consumer risks. Explicit approximations of the smallest number of units to be tested per lot and the maximum tolerable Nonconformity Score are presented. A Monte Carlo simulation approach is then used to find the most powerful decision rule for lot disposition. In case of over-dispersion, the suggested perspective allows the practitioners to greatly reduce the required sample size for lot sentencing. The developed methodology is applied to the manufacturing of glass for illustrative and comparative purposes.