A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments
A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments
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DOI：
10.1109/tsmc.2017.2672997
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Kit Yan Chan;H. Lam;C. Yiu;T. Dillon
中科院分区：
文献类型：
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作者：
Kit Yan Chan;H. Lam;C. Yiu;T. Dillon
新产品或服务的开发需要对与感知愉悦相关的美学品质的知识和理解。由于开发一项调查来评估新产品或服务的所有客观特征的审美质量是不切实际的，因此有必要开发一个模型来预测审美质量。本文提出了一种模糊回归方法，从一组给定的客观特征中预测审美质量，并考虑到人类评估的不确定性。该方法克服了统计回归只能预测质量量而不能预测质量不确定性的缺点。该方法还尝试改进传统的模糊回归方法，该方法模拟单一特征，其估计不确定性只能随着目标特征的增加而增加。提出的模糊回归方法采用遗传规划建立模型的非线性结构，并通过优化模糊准则确定模型系数。因此，所建立的模型可用于拟合样本量的非线性和不确定性。通过对具有不同采样性质和不同样本量的感知图像进行实例研究，评价了该方法的有效性和性能。本案例研究试图解决人类评估的不同特征。结果表明，当考虑模型特征和模糊准则时，所提出的模糊回归方法比现有的模糊回归方法具有更强的鲁棒性。
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.