Abstract The diagnosed features (samples) with multiple attributes of medical images always demand experts to reveal insight. Up to today, machine learning often cannot be a helpful expert. The reason lies in lacking evidential granules carrying knowledge and evidence for inferential learning. The shortage slows down representation learning which aims at discovering expressions for featuring concepts. Therefore, this paper proposes evidential memberships carrying preferential relevance to build a heuristic representation learning. Empirically, it solves local features and global representations with maximum coverage under challenges of shallow bury. For illustration, it is implemented on the testing data set of UCI-SPECTF.

中文翻译：

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基于证据成员资格的启发式表示学习：UCI-SPECTF 的案例研究

摘要 医学图像具有多种属性的诊断特征（样本）总是需要专家的洞察力。直到今天，机器学习通常还不能成为有用的专家。原因在于缺乏承载推理学习知识和证据的证据颗粒。这种短缺减慢了表征学习的速度，表征学习旨在发现特征概念的表达。因此，本文提出了具有优先相关性的证据成员资格来构建启发式表示学习。从经验上讲，它解决了浅埋挑战下具有最大覆盖范围的局部特征和全局表示。为了说明，它是在 UCI-SPECTF 的测试数据集上实现的。