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    A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

    253491.pdf (1.041Mb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Lam, H.
    Yiu, C.
    Dillon, T.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chan, K.Y. and Lam, H. and Yiu, C. and Dillon, T. 2017. A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 47 (8): pp. 2363-2377.
    Source Title
    IEEE Transactions on Systems, Man, and Cybernetics: Systems
    DOI
    10.1109/TSMC.2017.2672997
    ISSN
    2168-2216
    School
    Department of Electrical and Computer Engineering
    Remarks

    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/54714
    Collection
    • Curtin Research Publications
    Abstract

    Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence, the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account.

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