用进化算法自动筛选文献，兼顾准确与可解释性。
Automatic selection of primary studies in systematic reviews with evolutionary rule-based classification
- 基于语法引导的遗传编程生成可读规则，融合文本与引用数据
- 在多个数据集上达到90%以上准确率，优于主流方法
- 适合需要透明决策过程的科研综述或政策制定者
开展系统性文献综述时，搜索、筛选和分析科学文献耗时费力。随着人工智能发展，部分流程正逐步自动化。本文提出一种名为\ourmodel的进化机器学习方法，用于自动判断检索到的论文是否相关。该方法采用语法引导的遗传编程构建可解释的规则分类器，通过语法定义规则结构，能轻松融合传统文本信息与其他未被现有方法考虑的文献计量数据。实验表明，该方法可在不牺牲可解释性的前提下生成高精度分类器，并支持配置化信息源，实现前所未有的灵活性与准确性。
原文摘要 · Abstract (English)
Searching, filtering and analysing scientific literature are time-consuming tasks when performing a systematic literature review. With the rise of artificial intelligence, some steps in the review process are progressively being automated. In particular, machine learning for automatic paper selection can greatly reduce the effort required to identify relevant literature in scientific databases. We propose an evolutionary machine learning approach, called \ourmodel, to automatically determine whether a paper retrieved from a literature search process is relevant. \ourmodel builds an interpretable rule-based classifier using grammar-guided genetic programming. The use of a grammar to define the syntax and the structure of the rules allows \ourmodel to easily combine the usual textual information with other bibliometric data not considered by state-of-the-art methods. Our experiments demonstrate that it is possible to generate accurate classifiers without impairing interpretability and using configurable information sources not supported so far.
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