提出一种通用框架，用以表达模型在不同任务中的精确性与准确性意义。
The Representation of Meaningful Precision, and Accuracy
- 基于简约粗糙框架构建可组合的知识表示方法
- 解决传统指标在跨领域应用中的局限性问题
- 适用于多种任务场景，尤其适合认知相关研究
精确性与准确性的概念具有领域和问题依赖性。统计学习、多种机器学习及二分类或多分类任务中常用的简化数值硬/软度量，难以有效揭示模型的意义或相关性，且缺乏模式或证明力。目前认知领域也缺少对类似概念的良好度量或表达方式。本文反思关键问题，提出一种在简约通用粗糙框架下的可组合知识表示方法，该框架足够通用以覆盖大多数应用情境，并可在计算工具改进的背景下应用。
原文摘要 · Abstract (English)
The concepts of precision, and accuracy are domain and problem dependent. The simplified numeric hard and soft measures used in the fields of statistical learning, many types of machine learning, and binary or multiclass classification problems are known to be of limited use for understanding the meaningfulness of models or their relevance. Arguably, they are neither of patterns nor proofs. Further, there are no good measures or representations for analogous concepts in the cognition domain. In this research, the key issues are reflected upon, and a compositional knowledge representation approach in a minimalist general rough framework is proposed for the problem contexts. The latter is general enough to cover most application contexts, and may be applicable in the light of improved computational tools available.
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